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Location-Dependent Query Processing: Semantic Cache for Real-Time Smart City Analytics

Location-Dependent Query Processing: Semantic Cache for Real-Time Smart City Analytics Hindawi Applied Bionics and Biomechanics Volume 2021, Article ID 9958647, 9 pages https://doi.org/10.1155/2021/9958647 Research Article Location-Dependent Query Processing: Semantic Cache for Real-Time Smart City Analytics 1 1 1 2 Rabia Hasan, Waseem Shehzad, Ejaz Ahmed, Hasan Ali Khattak , 3 4 Ahmed S. AlGhamdi , and Sultan S. Alshamrani Department of Computer Science, National University of Computer & Emerging Sciences, Islamabad 45000, Pakistan School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 45000, Pakistan Department of Computer Engineering, Collage of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia Department of Information Technology, College of Computer and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia Correspondence should be addressed to Hasan Ali Khattak; hasan.alikhattak@seecs.edu.pk Received 18 October 2021; Revised 3 November 2021; Accepted 8 November 2021; Published 21 December 2021 Academic Editor: Fahd Abd Algalil Copyright © 2021 Rabia Hasan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. With the advent of wireless sensor networks and their deep integration with the world have enabled users worldwide to achieve benefits from location-based services through mobile applications, the problems such as low bandwidth, high network traffic, and disconnections issues are normally extracted from mobile services. An efficient database system is required to manage mentioned problems. Our research work finds the probability of user’s next locations. A mobile user (query issuer) changes its position when performing a specific mobile search, where these queries change and repeat the search with the issuer position. Moreover, the query issuer can be static and may perform searches with varying conditions of queries. Data is exchanged with mobile devices and questions that are formulated during searching for query issuer locations. An aim of the research work is achieved through effectively processing of queries in terms of location-dependent, originated by mobile users. Significant studies have been performed in this field in the last two decades. In this paper, our novel approach comprise of usage of semantic caches with the Bayesian networks using a prediction algorithm. Our approach is unique and distinct from the traditional query processing system especially in mobile domain for the prediction of future locations of users. Consequently, a better search is analyzed using the response time of data fetch from the cache. 1. Introduction works [2], vehicular networks [3], healthcare informatics [4], financial tech, and cloud computing [5], by managing a connection between mobile database systems and servers. The role of a mobile computing in computer science is Data is exchanged using mobile devices whereby queries significant that is in information, computing, and telecom- munication (ICT) domains. The ICT domain consists of are generated and processed. A new search is emerged in the query processing domain. This study consists of two cat- wireless networks with the mobile devices. Pervasive mobile applications generate new opportunities and challenges in egories in which different queries are used to access the data. Normally, queries along with their results and data are computing such as databases and networks. An excessive mapped with the current locations of mobile users, such usage of mobile applications and services is related to hardware technologies and wireless network systems [1]. queries are called location-related or geolocation-based queries. Queries rely on location information, where the Wireless applications plays an important role in social net- 2 Applied Bionics and Biomechanics Location location is passed as a parameter. In location-dependent related queries, the queries are transmitted with the change in loca- query tions (see Figure 1) [6]. A query consists of mobile client static object is managed in terms of client/user queries. For example, in query “where Location Location is the nearest restaurant?” there will be multiple locations or dependent aware location may be changed. Since the client/node issues the query query query for mobile, execution of queries may extract irrelevant locations for the client in the new location. Moreover, the client may change the location and the query response is no longer required. After processing, the query results need to be validated in the first type of query as the query issuer’s location can be changed, and the results may need calcula- tion accordingly [7]. Mobile client Mobile client Static client static objects mobile objects mobile objects Various use cases can be elaborated to understand the above-mentioned challenge [8]. However, with the change Figure 1: Classification of location-related queries. in locations, the response may vary. The results of some query at one location may be different from the results of This means the minimum processing is performed in terms the same query at some other location. For instance, if the user queries about the moving objects without changing its of improved response time. own position, this type of query is called static client mobile Answering the LDQs will help in understanding the objects. For example, another type of query would be “the requirements in paper with the following key characteristics number of all cars passed by a user”. Finally, when both user [12]: and objects are moving then query would be challenging to (i) A query user can be located through general packet process as both the client and objects are moving continu- radio service (GPRS) before processing the query ously, and information of both user and objects needs to be stored [9]. When both user and objects are moving such (ii) Keep track of the position of the user. Update the query can be handled easily before the first two types of database each time when a user changes the location query problems are resolved. Queries are location-aware in which location is already (iii) Location of user changes and database for location defined inside the query. For example, “the names of all hos- is updated. This keeps tracking of user’s location pitals in downtown” and “what is the distance to the airport (iv) Analyze and calculate the query results with the pre- from the main city?” Typically, these queries do not depend diction of future information. For validating the upon the location of the query issuer (user). If a user with a query results, the time interval between submitting mobile is moving and repeating the query frequently, the a query and sending results back is measured result can be different because the user’s location is changing [11]. The factor of processing the query is costly where com- (v) To process queries efficiently, technologies such as munications are often difficult to measure when user with caching, indexing, and data replication are used mobile devices is changing the location. The queries are (vi) The values of locations of users and prediction are called location-dependent queries (LDQ) [1, 10] in which not calculated correctly geographic locations of users are important. Similarly, using semantic cache schema an algorithm is introduced for divid- The remaining paper is structured as follows: Section 2 ing the queries into probability and remainder queries [12]. describes the related studies. The motivation towards LDQ LDQs are divided into three categories related to static and validations are covered in Section 3. Section 4 illustrates moving users and objects as shown in Figure 1. Bayesian networks using a prediction algorithm. Implemen- Continuous queries are those which are answered fre- tation methods are discussed in Section 5. Section 6 provides quently with the change in locations, mentioned inside the the experimental results followed by the conclusion in queries. In terms of mobile computing, such queries are Section 7. processed with the asymmetric features of mobile devices’ such as energy issues, low battery power, and less band- width. These features affect the processing of queries. 2. Related Work Besides, servers keep records of locations of all mobile devices and need to be updated when a mobile user/client Several issues and challenges in the processing of LDQs, e.g., changes its position. communication cost, are difficult to measure when mobile The purpose of this study is to adopt a mechanism that devices change their locations. Location management can predict and preprocess data to reduce the overall performs two operations, i.e., Lookups and Update. The response time using a prediction system flow described in Lookups retrieves the user/client location, whereas the Section 3. The usage of a cache memory helps in producing Update is needed in all sites in a mobile network. There results on the same next query issued by any mobile client. are different updating methods, and any site can be updated Applied Bionics and Biomechanics 3 problem is highlighted, i.e., the query text must be compared that shares the updated location with all other sites. More- over, each site, which increases the update cost, can be with cache descriptors to find the desired answer without updated individually. To solve the problem of users’ location evaluating the query. For experiments, the authors assumed information, the location binding method is used. The bind- a conservative algorithm that never produces false results. ing is managed by location-based services, which identify the The algorithm also processes queries by extracting simple location and bind it with the query. This gives rise to another expressions from the incoming query and matches it with problem of granularity mismatch [1]. cache descriptors. Still, there is a need to provide such a A suitable query language needs to be developed to database system that can predict and cache the most likely accessed data. Query processing must divide or break up a express the types of queries that include operators, e.g., close to and within. Caching is one of the techniques to solve the query to utilize the benefits of the cache [17]. data management issues. Whenever a client changes its posi- Another study in [18] presents a semantic cache tion, the data in mobile database systems remain no more arrangement, where such arrangement accesses location- valid. Therefore, caching is a technique through which we dependent destination (LDD) in mobile computing. Initially, a mobility model is used to represent mobile users’ moving can check data validation, whereas through semantic cach- ing, we can predict future results for further queries. Other behaviors and properly define LDD. Then, query processing limitations are low battery power, limited bandwidth, and and cache management tactics are examined. Finally, the frequent disconnections. proposed approach is evaluated using a simulation study. A semantic cache technique that stores data and its The evaluation purpose was to check the semantic caching scheme’s performance and its replacement strategy, named descriptions is proposed in [10]. It uses the Voronoi diagram to index data objects. The proposed method is used to FAR. The results indicate that semantic caching is more retrieve the nearest neighbor queries. It can be assumed from flexible. LDD is more effective to be used instead of page the experiments that the client location and speed are known caching. The performance of page caching is problematic from the GPRS and query issue time stamp. A large area is to the database’s physical organization. Moreover, the results also show that the semantic cache replacement strategy, i.e., divided into regions, and a V index is constructed for each of the service objects in the region. By measuring the client FAR, is robust to various types of workloads. Additionally, the study also addresses the problems in building an abstract speed, the next nearest neighbor service is predicted. The cache contains the information of query regions (usually a model of moving objects and formally defines the queries. circle) in which the client is the center of the circle, whereas The authors in [10] present a scalable system based on mobile agents by supporting a distributed processing of the radius is the shortest distance. When a query is submit- ted, the data can be collected from the cache. Otherwise, the LDQs in mobile environments [19]. The proposed system whole query is sent to the server. The results show the processes LDQs in a completely decentralized way without efficiency of this technique. However, when the number of overloading wireless user devices. It caters to scenarios service objects in a region increases, the cache hit ratio where users are issuing queries and other exciting objects are moving using a location prediction algorithm. Moreover, decreases. Moreover, if a client remains in the same region, all cached data is assumed to be valid. Nevertheless, as it it is well adapted to environments where location’s data is moves to another region, the cache will be cleared and distributed in a network and processing tasks can be per- updated with the new data [11]. formed in parallel. This way it allows high scalability. A The drawback of the semantic cache was overcome by a mobile agent can be incharge of tracking the location of interesting moving objects and refreshing the answer to a technique presented in [12], wherein the authors have pro- posed a semantic cache schema, which supports processing query efficiently. The system is evaluated through an exper- different types of queries. It was based on the VCKNN query iment by carrying out simulations of a sample scenario. processing algorithm and the cache item structure that The authors in [20] discuss that how mobile devices have decides which data to be stored. They also defined a cache captured the entire world’s coverage, leading to the demand for location-based services and applications used in daily management algorithm to calculate which part of the query can be answered by dividing the query into two types, i.e., routines. Location detection capability services constitute a probe and remainder [13]. Through a cache replacement significant part of mobile devices. The paper discusses an policy, cache items having a minimum number of references environment where mobile objects have no capability for can be replaced. It works the same as the traditional LRU location detection, and location-aware mobile sensors are scattered for sensing mobile devices’ presence. A sensor policy. They concluded that there should be a cache schema for the efficient utilization of the cache. can only detect and identify those mobile objects lie in their Grid-partition index has also been used to answer the range but cannot identify their exact locations. The sensor readings are aggregated and sent to the server at regular nearest neighbor query using semantic and hybrid caches [14]. Some of the benefits of using cache in distributed sys- intervals. The system supports mobile LDQs over mobile objects. tems are such that they are transparent for the application and do not affect the functionality of the application that Approximate Moving Range (AMR) query is presented utilizes a cache, as described in [13–16]. Unlike centralized in [11], which is a new class of location-based query that introduces a probabilistic technique for processing AMR. systems, data is stored in the cache in webpages. The web- pages give answers from a query called cache units and the The environment is based on mobile sensors, where each sensor is modeled as a moving rectangle representing its query itself is a cache descriptor. The query containment 4 Applied Bionics and Biomechanics cally evaluate the results of existing techniques in this sensing range. Each sensor can detect unaware location objects discovered in its range. The AMR query pointed at context. moving objects with no capability of location detection. The focus is to study the challenges and issues faced by LDQ processing [22]. Location-based queries can be classi- The database server can evaluate the AMR queries based on the detection of mobile sensors on moving objects. The fied into spatial queries or temporal queries, which can be proposed query was tested on different simulation studies further categorized into continuous or noncontinuous to evaluate its processing technique. The results showed that queries. Range queries include the objects lie within a spe- the AMR query processing technique is very efficient and cific region, while nearest neighbor queries include the objects lie closer to a specific region. Navigation queries reliable. It is also highly cost-effective and scalable than standard approaches. provide the path to users in a specific location. In this paper, In [19], the authors discuss that how mobile devices have different methods for LDQs and the problems faced in the made significant advances, providing their users with out- data management system have been discussed. Some chal- standing services. The study proposes a system that deals lenges are also possible in mobile systems in terms of data management. The methods presented to solve the issues with mobile environments and supports the processing of continuous LDQs. This research includes a new approach are caching and broadcasting data. Caching techniques for continuous LDQs in a wireless environment with decen- make accessing data fast and lesser the network traffic tralized solutions for continuous moving queries. The sys- caused by processing the LDQs. Broadcasting of data means tem was based on tracking related moving objects with the transferring information to a large number of users over a mobile network. One method is such that the server broad- help of mobile agents. When a user enquires a query in the system, the answer is refreshed after a certain period, and casts the invalidation report, while the other is that every the network of agents adjusts itself to provide data as new mobile device receives the updated data if its value is chan- as possible. ged on the server. There is a local database between a mobile The system’s main features include the following: (1) a and the central server that acts as a mediator. In [23], the authors propose a query formalization tech- flexible and distributed architecture is required that could be scalable when the number of moving objects and proxies nique that uses both location-dependent and location- increases in specific scenarios. For a considerable number of independent query models. It provides a general view of location-related queries. It also distinguishes between loca- moving objects, the centralized approach is not feasible. (2) To avoid wireless user devices with overloading processing tion dependence and location independence. The proposed approach provides implicit translation of both LDQs and tasks, queries were performed on proxies and fixed networks instead of wireless networks. (3) Any object in the scenario location-aware queries. Moreover, a software architectural can access the location query. (4) Query answers are contin- style, named Location Dependent Services Manager (LDSM), is also proposed. This architecture aims to aid in uously updated and selected by a user because the location data is changing dynamically. The proposed system’s main the translation process. Many personalized techniques have been widely studied advantage is a general solution for processing LDQs for users enquiring any query. Besides, the system is efficient that use the auto recommendation system [24]. Mobile sys- concerning processing continuous queries. Moreover, it is tems provide location-based services to users based on their physical locations. In this paper, a recommendation system adaptable to environments where data location is distributed over a network providing scalability to the system and opti- has been built, which is map-based to overcome all the abovementioned problems. Mobile users face problems due mizing wireless communications. In [21], a scheme is proposed that provides a compre- to small screens and interfaces of mobiles. This technique hensive study on mobile database systems, their characteris- improves the interface system. Old recommendation sys- tems use collaborative filtering, i.e., similar users have simi- tics, and architecture for query processing in mobile databases. Apart from describing the existing architecture lar interests. This research uses a map-based interface close challenges, the authors have investigated the location privacy to user familiarization to overcome the issues of visualizing protection concerns associated with mobile database query and resource usage of mobile devices. Today, people face processing. Mobile database systems are generally consid- many problems in utilizing navigation services due to mobiles’ bad interfaces and low usage of resources. The pre- ered as an extension of distributed database systems but differ in terms of mobile environments’ constraints. The sented system collects the information, time, location, and constraints involve power restriction and frequent discon- weather of the user’s physical location and recommends nections. Other fundamental problems are significantly the desired result in the form of a map. The study presented associated with mobile database systems, i.e., scalability the BN-based recommendation system, which reflects the user’s recommendation using information from mobile (low bandwidth is covering long transactions). Based on query processing, mobile database systems have three layers, devices user profiles. i.e., the Application layer, the Middleware layer (query, In [25], a novel approach has been proposed to enhance cache, network), and the Database layer. The best noticeable and extend Location Base Service (LBS)’s privacy to users. technique in the context of location privacy for LDQs is Information was extracted from LBS queries with regards K-anonymity. However, privacy concerns are still an open to service providers. Authors have developed and evaluated area of research as a little study has been accomplished to MobiCrowd, a scheme that enables LBS users to hide in produce efficient algorithms. There is also a need to empiri- the crowd and reduce their exposure while continuing to Applied Bionics and Biomechanics 5 receive the location context information they need. Mobi- 02 Cache Crowd achieves this by relying on the collaboration between 01 1 Query about 01 users who have the incentive and the capability to safeguard 02 03 2 Query about their privacy. In this study, a novel analytical framework is User proposed to quantify the location privacy of distributed pro- 03 tocol. The epidemic model captures the hiding probability for user locations, i.e., the fraction of times when the adver- sary does not observe user queries due to MobiCrowd. In User this model, the Bayesian inference attack estimates the loca- tion of users when they hide. The extensive combination of epidemic and Bayesian analysis elaborates a significant Figure 2: Mobile client static objects. improvement across both individual and average mobility prior knowledge scenarios for the adversary. The authors in [26] propose a semantic cache schema to extend the domain of query types. Semantic cache schema Get query answers all types of queries based on the query graph model and query conversions to QGM. Semantic cache schema, data structure of cache items, cache management algorithm, Store and cache items replacement algorithm are designed by in cache Check Predict and planning the semantic cache schema. The semantic cache cache contents pre-fetch from server schema gains significance in performance, while other Input methods are evaluated using simulations. Response time, next query data volume transferred from the server, and the number of connections to the server are the performance parameters Is data No that were measured and compared significantly. The perfor- Send query to server validated? mance is typically expected when submitted queries are more dependent on each other semantically. An application Yes domain extends and broads in mobile systems with the study of elimination of this environment’s limitations. There Show the results are several semantic cache schemas developed with simple query types. There is a need for a semantic cache schema Figure 3: Flowchart of the proposed system. that can answer all queries as a complete solution. stored on a local cache, i.e., a mobile host cache. The predic- 3. Motivation tion of the user’s next location maybe based on data classifi- cation and prediction algorithms. When a mobile client A mobile user/client is in region R1, and queries are about issues the next query, its results will be calculated from the object1. The results of query1 are stored in the local cache. cached data if the prediction is accurate and there will be Now, when a user changes its location from regions R1 to no need to send the query to the server. It will be useful to R3 and generates the same query, the data stored in the use semantic cache for storing both query text and data. cache remains no more valid, as the query needed to be There is a possibility that the future location’s prediction answered according to its current location. So, we must send of a user is false if a mobile client moves towards the new the query again to the server to process, gather the desired location which was never stored in the history. Therefore, data, and store it in the cache. As mentioned earlier, results we can predict the results based on history or patterns of are needed to be validated in LDQs whenever the user issuing query of other mobile clients in that region. changes its location. Figure 2 depicts this problem in a sim- plified way. To utilize the cache to benefit us in reducing the network 4. Location Prediction Using traffic and decrease the response time, we propose a tech- Bayesian Networks nique that can predict the future location, prefetch the data, and store it in the cache. The flowchart presented in Figure 3 The proposed technique manages the primary database sys- describes the working of the proposed system. This helps in tem and describes the work performed on mobile database evaluating the response time when the data of queries is systems in the query processing. To reduce the workload saved in the cache. and processing on mobiles, it is highly recommended in this Location server tracks mobile users and records their research that the minimum processing should be managed history based on their locations. All locations covered by a on mobile devices, keeping in view their issues, i.e., low mobile client are recorded and saved. Whenever a client bandwidth and limited battery. The proposed method’s issues any LDQ, the database system will send the desired main idea is to examine the users’ moving patterns accord- result to the mobile host and the future prediction of the ing to the service objects. For this purpose, the history of user’s next coming location. The predicted data will be users will be recorded to train the system. The prediction 6 Applied Bionics and Biomechanics of the user next locations will be based on their current loca- O11 tions and the queried objects. The central part of the processing has been diverted to the primary database system. Since our goal is to improve the response time, some modifications were also needed on the server side for this purpose. The proposed technique D C has utilized the Bayesian network to reduce processing time. All the locations and service objects related to their locations are stored in tables on the DBS server. Using Bayesian net- work, all tables containing information of service objects I H G according to their locations are converted into conditional probability tables (CPT). The Bayesian network has two components, i.e., struc- ture and parameters. The structure includes directed ayclic Figure 4: Directed acyclic graph. graphs (DAG) while the parameter is a set of CPT, as pre- sented in Figure 4. It is imperative to note that this research focuses only on A P(A) one type of LDQ, i.e., mobile client static objects. When P(C) P(C P(C)) T 0.2 moving from loc to loc , the object always remains the same, i j A B O1 C=1 C=0 F 0.8 but location can vary from loc where i =0,1, 2,3, ⋯n. Only TT T ? ? the next location of the user will be predicted. For example, (a) if a user is in loci and queries about “find the nearest hotel,” TT F ? ? 01 P(O1) then only the user’s next possible location will be predicted, TF T? ? T 0.9 wherein the object is always “hotel.” The query for process- TF F ? ? ing and prediction remains the same until the user submits F 0.1 FT T ? ? the query with different service objects. Figure 5 shows prior (b) probabilities of nodes A and B. FT F ? ? Algorithm 1 describes the proposed technique in a FF T ? ? B P(B) simplified fashion. T 0.6 FFF? ? F 0.4 5. Implementation (c) All nodes shown in Figure 4 are dependent based on location Figure 5: (a)–(c) show the prior probabilities of node A and node and the object queries. To predict the next location, we cal- B, which represent locations in a region. (c) shows the posterior culate the posterior probabilities. For any dependent node probabilities (missing values) that are yet to be calculated. (location), we can calculate the probability as follows: If the next possible location of the user is Hi, where i = (iii) The database system will generate the desired result 1,2,3 ⋯ n, then on the server side and, in parallel, the data may be evaluated using a Bayesian network PA Hi P Hi ðÞ j Þ ðÞ P Hi ∣ A = ð1Þ ðÞ (iv) The user is at location A and queries about object X. PA ðÞ The probability of moving to the next location C will be as follows: The following example shows to understand that how the prediction can be performed at run time. In a region, PC Xi PXi ðÞ j Þ ðÞ PCðÞ ∣ X = : ð2Þ the user is currently at location A and queries about object PC ðÞ O1. The query processing process is defined in the following steps: (i) On a mobile database system, the query can be eval- Similarly, the probabilities of the dependent nodes can uated whether it may be answered through cache or be calculated, and the next location may be selected with not. If the results are obtained, the query can be the highest probability as follows: processed on a mobile system and no data will be sent to the server side (i) The server will now send back the results along with the predicted data (ii) In other cases, if the cache does not hold the valid data, the query can be sent to the server along with (ii) On receiving, a mobile device will show the output, the user’s current location, which the location man- and the prefetched data can be resided in the cache ager can determine to answer the next query Applied Bionics and Biomechanics 7 Nodes Refers to Algorithm: A E A Old city begin. while (get next query) B Anarkali loop. C Lake road match the query with cache contents case D Lahore zoo if matched E Iqbal town B C D collect the output send request to server to pre-fetch next data else –if not matched Figure 7: Sample graph 2 for datasets. submit the query to DB server store the pre-fetch data to cache store query as dscriptor in cache Table 1: Data set with three types of locations. collect the output end-if Previous location Current location Next location end case (given) (given) (prediction) end loop AB C exit BC D end CD E Algorithm 1: Query processing using semantic cache. Table 2: Results of two graphs using Weka tool. A C DATASETS GRAPH #1 GRAPH #2 Dataset 1 68.80% 86.69% Nodes Refers to Dataset 2 76.00% 87.88% A Old city F D B Anarkali Dataset 3 77% Not available C Lake road D Lahore zoo Dataset 4 70.80% Not available E Iqbal town F Sadar bazar G Muslim town E G H H Gulshan-E-iqbal Table 1 has two attributes, i.e., previous, and current location. With the help of these, we will predict its next loca- Figure 6: Sample graph 1 for datasets. tion (class attribute). The actual dataset has 100 instances, which we used for training and testing of the Bayesian net- (iii) As soon as the user changes its location from A to work. Four datasets are presented for graph 1, while two any other one, the previous data will be no more are displayed for the graph in Figure 7. Following is an valid. According to the moving speed of the user, example of how it works in a Bayesian network. First, we will mobile D.B. regenerates the query for the new loca- compute the required probabilities from the given dataset. tion. The new query will again match with the cache Next, these probabilities help us predict the user’s next mov- descriptors to get the results ing location to C, given A and B. According to the predicted location, the following location data will be prefetched and (iv) If the prediction is accurate, a signal will be sent to saved in the cache. Table 2 depicts both graphs’ mean the server to notify it, where all the values of CPTs future-based probability with datasets. Thus, for mobile will be updated P2P services, we have observed that the range queries have shown incremental progress while performing the query 6. Discussion results [27]. Figure 6 represents two different graph locations, which use datasets in the Weka tool. At the same time, the similarity 7. Conclusion probabilities are shown in Figure 7. Figure 7 shows all locations, named A, B, C, D, and E, The processing of Location-Dependent Query (LDQ) pro- within the city. The arrows in the graph show the roads cessing has been more challenging considering the massive between two locations. If a user is at location A and moves increase in the usage patterns shifting massively towards from A to B, then we have its previous and current locations mobile clients. Hence, there is always a need for an efficient saved as a given probability, and the user’s next location may prediction algorithm that can determine accurate results be predicted, which can be either C or B. The probability of based on context. This will also provide better performance moving to location C is known as posterior probability, concerning time complexity for more extensive databases. which a Bayesian network will predict. For this purpose, this study focuses on predicting the future 8 Applied Bionics and Biomechanics [8] D. Thomas and S. M. Thampi, “Mobile query processing- based on the history of mobile users. 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Location-Dependent Query Processing: Semantic Cache for Real-Time Smart City Analytics

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Copyright © 2021 Rabia Hasan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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1754-2103
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10.1155/2021/9958647
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Hindawi Applied Bionics and Biomechanics Volume 2021, Article ID 9958647, 9 pages https://doi.org/10.1155/2021/9958647 Research Article Location-Dependent Query Processing: Semantic Cache for Real-Time Smart City Analytics 1 1 1 2 Rabia Hasan, Waseem Shehzad, Ejaz Ahmed, Hasan Ali Khattak , 3 4 Ahmed S. AlGhamdi , and Sultan S. Alshamrani Department of Computer Science, National University of Computer & Emerging Sciences, Islamabad 45000, Pakistan School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 45000, Pakistan Department of Computer Engineering, Collage of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia Department of Information Technology, College of Computer and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia Correspondence should be addressed to Hasan Ali Khattak; hasan.alikhattak@seecs.edu.pk Received 18 October 2021; Revised 3 November 2021; Accepted 8 November 2021; Published 21 December 2021 Academic Editor: Fahd Abd Algalil Copyright © 2021 Rabia Hasan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. With the advent of wireless sensor networks and their deep integration with the world have enabled users worldwide to achieve benefits from location-based services through mobile applications, the problems such as low bandwidth, high network traffic, and disconnections issues are normally extracted from mobile services. An efficient database system is required to manage mentioned problems. Our research work finds the probability of user’s next locations. A mobile user (query issuer) changes its position when performing a specific mobile search, where these queries change and repeat the search with the issuer position. Moreover, the query issuer can be static and may perform searches with varying conditions of queries. Data is exchanged with mobile devices and questions that are formulated during searching for query issuer locations. An aim of the research work is achieved through effectively processing of queries in terms of location-dependent, originated by mobile users. Significant studies have been performed in this field in the last two decades. In this paper, our novel approach comprise of usage of semantic caches with the Bayesian networks using a prediction algorithm. Our approach is unique and distinct from the traditional query processing system especially in mobile domain for the prediction of future locations of users. Consequently, a better search is analyzed using the response time of data fetch from the cache. 1. Introduction works [2], vehicular networks [3], healthcare informatics [4], financial tech, and cloud computing [5], by managing a connection between mobile database systems and servers. The role of a mobile computing in computer science is Data is exchanged using mobile devices whereby queries significant that is in information, computing, and telecom- munication (ICT) domains. The ICT domain consists of are generated and processed. A new search is emerged in the query processing domain. This study consists of two cat- wireless networks with the mobile devices. Pervasive mobile applications generate new opportunities and challenges in egories in which different queries are used to access the data. Normally, queries along with their results and data are computing such as databases and networks. An excessive mapped with the current locations of mobile users, such usage of mobile applications and services is related to hardware technologies and wireless network systems [1]. queries are called location-related or geolocation-based queries. Queries rely on location information, where the Wireless applications plays an important role in social net- 2 Applied Bionics and Biomechanics Location location is passed as a parameter. In location-dependent related queries, the queries are transmitted with the change in loca- query tions (see Figure 1) [6]. A query consists of mobile client static object is managed in terms of client/user queries. For example, in query “where Location Location is the nearest restaurant?” there will be multiple locations or dependent aware location may be changed. Since the client/node issues the query query query for mobile, execution of queries may extract irrelevant locations for the client in the new location. Moreover, the client may change the location and the query response is no longer required. After processing, the query results need to be validated in the first type of query as the query issuer’s location can be changed, and the results may need calcula- tion accordingly [7]. Mobile client Mobile client Static client static objects mobile objects mobile objects Various use cases can be elaborated to understand the above-mentioned challenge [8]. However, with the change Figure 1: Classification of location-related queries. in locations, the response may vary. The results of some query at one location may be different from the results of This means the minimum processing is performed in terms the same query at some other location. For instance, if the user queries about the moving objects without changing its of improved response time. own position, this type of query is called static client mobile Answering the LDQs will help in understanding the objects. For example, another type of query would be “the requirements in paper with the following key characteristics number of all cars passed by a user”. Finally, when both user [12]: and objects are moving then query would be challenging to (i) A query user can be located through general packet process as both the client and objects are moving continu- radio service (GPRS) before processing the query ously, and information of both user and objects needs to be stored [9]. When both user and objects are moving such (ii) Keep track of the position of the user. Update the query can be handled easily before the first two types of database each time when a user changes the location query problems are resolved. Queries are location-aware in which location is already (iii) Location of user changes and database for location defined inside the query. For example, “the names of all hos- is updated. This keeps tracking of user’s location pitals in downtown” and “what is the distance to the airport (iv) Analyze and calculate the query results with the pre- from the main city?” Typically, these queries do not depend diction of future information. For validating the upon the location of the query issuer (user). If a user with a query results, the time interval between submitting mobile is moving and repeating the query frequently, the a query and sending results back is measured result can be different because the user’s location is changing [11]. The factor of processing the query is costly where com- (v) To process queries efficiently, technologies such as munications are often difficult to measure when user with caching, indexing, and data replication are used mobile devices is changing the location. The queries are (vi) The values of locations of users and prediction are called location-dependent queries (LDQ) [1, 10] in which not calculated correctly geographic locations of users are important. Similarly, using semantic cache schema an algorithm is introduced for divid- The remaining paper is structured as follows: Section 2 ing the queries into probability and remainder queries [12]. describes the related studies. The motivation towards LDQ LDQs are divided into three categories related to static and validations are covered in Section 3. Section 4 illustrates moving users and objects as shown in Figure 1. Bayesian networks using a prediction algorithm. Implemen- Continuous queries are those which are answered fre- tation methods are discussed in Section 5. Section 6 provides quently with the change in locations, mentioned inside the the experimental results followed by the conclusion in queries. In terms of mobile computing, such queries are Section 7. processed with the asymmetric features of mobile devices’ such as energy issues, low battery power, and less band- width. These features affect the processing of queries. 2. Related Work Besides, servers keep records of locations of all mobile devices and need to be updated when a mobile user/client Several issues and challenges in the processing of LDQs, e.g., changes its position. communication cost, are difficult to measure when mobile The purpose of this study is to adopt a mechanism that devices change their locations. Location management can predict and preprocess data to reduce the overall performs two operations, i.e., Lookups and Update. The response time using a prediction system flow described in Lookups retrieves the user/client location, whereas the Section 3. The usage of a cache memory helps in producing Update is needed in all sites in a mobile network. There results on the same next query issued by any mobile client. are different updating methods, and any site can be updated Applied Bionics and Biomechanics 3 problem is highlighted, i.e., the query text must be compared that shares the updated location with all other sites. More- over, each site, which increases the update cost, can be with cache descriptors to find the desired answer without updated individually. To solve the problem of users’ location evaluating the query. For experiments, the authors assumed information, the location binding method is used. The bind- a conservative algorithm that never produces false results. ing is managed by location-based services, which identify the The algorithm also processes queries by extracting simple location and bind it with the query. This gives rise to another expressions from the incoming query and matches it with problem of granularity mismatch [1]. cache descriptors. Still, there is a need to provide such a A suitable query language needs to be developed to database system that can predict and cache the most likely accessed data. Query processing must divide or break up a express the types of queries that include operators, e.g., close to and within. Caching is one of the techniques to solve the query to utilize the benefits of the cache [17]. data management issues. Whenever a client changes its posi- Another study in [18] presents a semantic cache tion, the data in mobile database systems remain no more arrangement, where such arrangement accesses location- valid. Therefore, caching is a technique through which we dependent destination (LDD) in mobile computing. Initially, a mobility model is used to represent mobile users’ moving can check data validation, whereas through semantic cach- ing, we can predict future results for further queries. Other behaviors and properly define LDD. Then, query processing limitations are low battery power, limited bandwidth, and and cache management tactics are examined. Finally, the frequent disconnections. proposed approach is evaluated using a simulation study. A semantic cache technique that stores data and its The evaluation purpose was to check the semantic caching scheme’s performance and its replacement strategy, named descriptions is proposed in [10]. It uses the Voronoi diagram to index data objects. The proposed method is used to FAR. The results indicate that semantic caching is more retrieve the nearest neighbor queries. It can be assumed from flexible. LDD is more effective to be used instead of page the experiments that the client location and speed are known caching. The performance of page caching is problematic from the GPRS and query issue time stamp. A large area is to the database’s physical organization. Moreover, the results also show that the semantic cache replacement strategy, i.e., divided into regions, and a V index is constructed for each of the service objects in the region. By measuring the client FAR, is robust to various types of workloads. Additionally, the study also addresses the problems in building an abstract speed, the next nearest neighbor service is predicted. The cache contains the information of query regions (usually a model of moving objects and formally defines the queries. circle) in which the client is the center of the circle, whereas The authors in [10] present a scalable system based on mobile agents by supporting a distributed processing of the radius is the shortest distance. When a query is submit- ted, the data can be collected from the cache. Otherwise, the LDQs in mobile environments [19]. The proposed system whole query is sent to the server. The results show the processes LDQs in a completely decentralized way without efficiency of this technique. However, when the number of overloading wireless user devices. It caters to scenarios service objects in a region increases, the cache hit ratio where users are issuing queries and other exciting objects are moving using a location prediction algorithm. Moreover, decreases. Moreover, if a client remains in the same region, all cached data is assumed to be valid. Nevertheless, as it it is well adapted to environments where location’s data is moves to another region, the cache will be cleared and distributed in a network and processing tasks can be per- updated with the new data [11]. formed in parallel. This way it allows high scalability. A The drawback of the semantic cache was overcome by a mobile agent can be incharge of tracking the location of interesting moving objects and refreshing the answer to a technique presented in [12], wherein the authors have pro- posed a semantic cache schema, which supports processing query efficiently. The system is evaluated through an exper- different types of queries. It was based on the VCKNN query iment by carrying out simulations of a sample scenario. processing algorithm and the cache item structure that The authors in [20] discuss that how mobile devices have decides which data to be stored. They also defined a cache captured the entire world’s coverage, leading to the demand for location-based services and applications used in daily management algorithm to calculate which part of the query can be answered by dividing the query into two types, i.e., routines. Location detection capability services constitute a probe and remainder [13]. Through a cache replacement significant part of mobile devices. The paper discusses an policy, cache items having a minimum number of references environment where mobile objects have no capability for can be replaced. It works the same as the traditional LRU location detection, and location-aware mobile sensors are scattered for sensing mobile devices’ presence. A sensor policy. They concluded that there should be a cache schema for the efficient utilization of the cache. can only detect and identify those mobile objects lie in their Grid-partition index has also been used to answer the range but cannot identify their exact locations. The sensor readings are aggregated and sent to the server at regular nearest neighbor query using semantic and hybrid caches [14]. Some of the benefits of using cache in distributed sys- intervals. The system supports mobile LDQs over mobile objects. tems are such that they are transparent for the application and do not affect the functionality of the application that Approximate Moving Range (AMR) query is presented utilizes a cache, as described in [13–16]. Unlike centralized in [11], which is a new class of location-based query that introduces a probabilistic technique for processing AMR. systems, data is stored in the cache in webpages. The web- pages give answers from a query called cache units and the The environment is based on mobile sensors, where each sensor is modeled as a moving rectangle representing its query itself is a cache descriptor. The query containment 4 Applied Bionics and Biomechanics cally evaluate the results of existing techniques in this sensing range. Each sensor can detect unaware location objects discovered in its range. The AMR query pointed at context. moving objects with no capability of location detection. The focus is to study the challenges and issues faced by LDQ processing [22]. Location-based queries can be classi- The database server can evaluate the AMR queries based on the detection of mobile sensors on moving objects. The fied into spatial queries or temporal queries, which can be proposed query was tested on different simulation studies further categorized into continuous or noncontinuous to evaluate its processing technique. The results showed that queries. Range queries include the objects lie within a spe- the AMR query processing technique is very efficient and cific region, while nearest neighbor queries include the objects lie closer to a specific region. Navigation queries reliable. It is also highly cost-effective and scalable than standard approaches. provide the path to users in a specific location. In this paper, In [19], the authors discuss that how mobile devices have different methods for LDQs and the problems faced in the made significant advances, providing their users with out- data management system have been discussed. Some chal- standing services. The study proposes a system that deals lenges are also possible in mobile systems in terms of data management. The methods presented to solve the issues with mobile environments and supports the processing of continuous LDQs. This research includes a new approach are caching and broadcasting data. Caching techniques for continuous LDQs in a wireless environment with decen- make accessing data fast and lesser the network traffic tralized solutions for continuous moving queries. The sys- caused by processing the LDQs. Broadcasting of data means tem was based on tracking related moving objects with the transferring information to a large number of users over a mobile network. One method is such that the server broad- help of mobile agents. When a user enquires a query in the system, the answer is refreshed after a certain period, and casts the invalidation report, while the other is that every the network of agents adjusts itself to provide data as new mobile device receives the updated data if its value is chan- as possible. ged on the server. There is a local database between a mobile The system’s main features include the following: (1) a and the central server that acts as a mediator. In [23], the authors propose a query formalization tech- flexible and distributed architecture is required that could be scalable when the number of moving objects and proxies nique that uses both location-dependent and location- increases in specific scenarios. For a considerable number of independent query models. It provides a general view of location-related queries. It also distinguishes between loca- moving objects, the centralized approach is not feasible. (2) To avoid wireless user devices with overloading processing tion dependence and location independence. The proposed approach provides implicit translation of both LDQs and tasks, queries were performed on proxies and fixed networks instead of wireless networks. (3) Any object in the scenario location-aware queries. Moreover, a software architectural can access the location query. (4) Query answers are contin- style, named Location Dependent Services Manager (LDSM), is also proposed. This architecture aims to aid in uously updated and selected by a user because the location data is changing dynamically. The proposed system’s main the translation process. Many personalized techniques have been widely studied advantage is a general solution for processing LDQs for users enquiring any query. Besides, the system is efficient that use the auto recommendation system [24]. Mobile sys- concerning processing continuous queries. Moreover, it is tems provide location-based services to users based on their physical locations. In this paper, a recommendation system adaptable to environments where data location is distributed over a network providing scalability to the system and opti- has been built, which is map-based to overcome all the abovementioned problems. Mobile users face problems due mizing wireless communications. In [21], a scheme is proposed that provides a compre- to small screens and interfaces of mobiles. This technique hensive study on mobile database systems, their characteris- improves the interface system. Old recommendation sys- tems use collaborative filtering, i.e., similar users have simi- tics, and architecture for query processing in mobile databases. Apart from describing the existing architecture lar interests. This research uses a map-based interface close challenges, the authors have investigated the location privacy to user familiarization to overcome the issues of visualizing protection concerns associated with mobile database query and resource usage of mobile devices. Today, people face processing. Mobile database systems are generally consid- many problems in utilizing navigation services due to mobiles’ bad interfaces and low usage of resources. The pre- ered as an extension of distributed database systems but differ in terms of mobile environments’ constraints. The sented system collects the information, time, location, and constraints involve power restriction and frequent discon- weather of the user’s physical location and recommends nections. Other fundamental problems are significantly the desired result in the form of a map. The study presented associated with mobile database systems, i.e., scalability the BN-based recommendation system, which reflects the user’s recommendation using information from mobile (low bandwidth is covering long transactions). Based on query processing, mobile database systems have three layers, devices user profiles. i.e., the Application layer, the Middleware layer (query, In [25], a novel approach has been proposed to enhance cache, network), and the Database layer. The best noticeable and extend Location Base Service (LBS)’s privacy to users. technique in the context of location privacy for LDQs is Information was extracted from LBS queries with regards K-anonymity. However, privacy concerns are still an open to service providers. Authors have developed and evaluated area of research as a little study has been accomplished to MobiCrowd, a scheme that enables LBS users to hide in produce efficient algorithms. There is also a need to empiri- the crowd and reduce their exposure while continuing to Applied Bionics and Biomechanics 5 receive the location context information they need. Mobi- 02 Cache Crowd achieves this by relying on the collaboration between 01 1 Query about 01 users who have the incentive and the capability to safeguard 02 03 2 Query about their privacy. In this study, a novel analytical framework is User proposed to quantify the location privacy of distributed pro- 03 tocol. The epidemic model captures the hiding probability for user locations, i.e., the fraction of times when the adver- sary does not observe user queries due to MobiCrowd. In User this model, the Bayesian inference attack estimates the loca- tion of users when they hide. The extensive combination of epidemic and Bayesian analysis elaborates a significant Figure 2: Mobile client static objects. improvement across both individual and average mobility prior knowledge scenarios for the adversary. The authors in [26] propose a semantic cache schema to extend the domain of query types. Semantic cache schema Get query answers all types of queries based on the query graph model and query conversions to QGM. Semantic cache schema, data structure of cache items, cache management algorithm, Store and cache items replacement algorithm are designed by in cache Check Predict and planning the semantic cache schema. The semantic cache cache contents pre-fetch from server schema gains significance in performance, while other Input methods are evaluated using simulations. Response time, next query data volume transferred from the server, and the number of connections to the server are the performance parameters Is data No that were measured and compared significantly. The perfor- Send query to server validated? mance is typically expected when submitted queries are more dependent on each other semantically. An application Yes domain extends and broads in mobile systems with the study of elimination of this environment’s limitations. There Show the results are several semantic cache schemas developed with simple query types. There is a need for a semantic cache schema Figure 3: Flowchart of the proposed system. that can answer all queries as a complete solution. stored on a local cache, i.e., a mobile host cache. The predic- 3. Motivation tion of the user’s next location maybe based on data classifi- cation and prediction algorithms. When a mobile client A mobile user/client is in region R1, and queries are about issues the next query, its results will be calculated from the object1. The results of query1 are stored in the local cache. cached data if the prediction is accurate and there will be Now, when a user changes its location from regions R1 to no need to send the query to the server. It will be useful to R3 and generates the same query, the data stored in the use semantic cache for storing both query text and data. cache remains no more valid, as the query needed to be There is a possibility that the future location’s prediction answered according to its current location. So, we must send of a user is false if a mobile client moves towards the new the query again to the server to process, gather the desired location which was never stored in the history. Therefore, data, and store it in the cache. As mentioned earlier, results we can predict the results based on history or patterns of are needed to be validated in LDQs whenever the user issuing query of other mobile clients in that region. changes its location. Figure 2 depicts this problem in a sim- plified way. To utilize the cache to benefit us in reducing the network 4. Location Prediction Using traffic and decrease the response time, we propose a tech- Bayesian Networks nique that can predict the future location, prefetch the data, and store it in the cache. The flowchart presented in Figure 3 The proposed technique manages the primary database sys- describes the working of the proposed system. This helps in tem and describes the work performed on mobile database evaluating the response time when the data of queries is systems in the query processing. To reduce the workload saved in the cache. and processing on mobiles, it is highly recommended in this Location server tracks mobile users and records their research that the minimum processing should be managed history based on their locations. All locations covered by a on mobile devices, keeping in view their issues, i.e., low mobile client are recorded and saved. Whenever a client bandwidth and limited battery. The proposed method’s issues any LDQ, the database system will send the desired main idea is to examine the users’ moving patterns accord- result to the mobile host and the future prediction of the ing to the service objects. For this purpose, the history of user’s next coming location. The predicted data will be users will be recorded to train the system. The prediction 6 Applied Bionics and Biomechanics of the user next locations will be based on their current loca- O11 tions and the queried objects. The central part of the processing has been diverted to the primary database system. Since our goal is to improve the response time, some modifications were also needed on the server side for this purpose. The proposed technique D C has utilized the Bayesian network to reduce processing time. All the locations and service objects related to their locations are stored in tables on the DBS server. Using Bayesian net- work, all tables containing information of service objects I H G according to their locations are converted into conditional probability tables (CPT). The Bayesian network has two components, i.e., struc- ture and parameters. The structure includes directed ayclic Figure 4: Directed acyclic graph. graphs (DAG) while the parameter is a set of CPT, as pre- sented in Figure 4. It is imperative to note that this research focuses only on A P(A) one type of LDQ, i.e., mobile client static objects. When P(C) P(C P(C)) T 0.2 moving from loc to loc , the object always remains the same, i j A B O1 C=1 C=0 F 0.8 but location can vary from loc where i =0,1, 2,3, ⋯n. Only TT T ? ? the next location of the user will be predicted. For example, (a) if a user is in loci and queries about “find the nearest hotel,” TT F ? ? 01 P(O1) then only the user’s next possible location will be predicted, TF T? ? T 0.9 wherein the object is always “hotel.” The query for process- TF F ? ? ing and prediction remains the same until the user submits F 0.1 FT T ? ? the query with different service objects. Figure 5 shows prior (b) probabilities of nodes A and B. FT F ? ? Algorithm 1 describes the proposed technique in a FF T ? ? B P(B) simplified fashion. T 0.6 FFF? ? F 0.4 5. Implementation (c) All nodes shown in Figure 4 are dependent based on location Figure 5: (a)–(c) show the prior probabilities of node A and node and the object queries. To predict the next location, we cal- B, which represent locations in a region. (c) shows the posterior culate the posterior probabilities. For any dependent node probabilities (missing values) that are yet to be calculated. (location), we can calculate the probability as follows: If the next possible location of the user is Hi, where i = (iii) The database system will generate the desired result 1,2,3 ⋯ n, then on the server side and, in parallel, the data may be evaluated using a Bayesian network PA Hi P Hi ðÞ j Þ ðÞ P Hi ∣ A = ð1Þ ðÞ (iv) The user is at location A and queries about object X. PA ðÞ The probability of moving to the next location C will be as follows: The following example shows to understand that how the prediction can be performed at run time. In a region, PC Xi PXi ðÞ j Þ ðÞ PCðÞ ∣ X = : ð2Þ the user is currently at location A and queries about object PC ðÞ O1. The query processing process is defined in the following steps: (i) On a mobile database system, the query can be eval- Similarly, the probabilities of the dependent nodes can uated whether it may be answered through cache or be calculated, and the next location may be selected with not. If the results are obtained, the query can be the highest probability as follows: processed on a mobile system and no data will be sent to the server side (i) The server will now send back the results along with the predicted data (ii) In other cases, if the cache does not hold the valid data, the query can be sent to the server along with (ii) On receiving, a mobile device will show the output, the user’s current location, which the location man- and the prefetched data can be resided in the cache ager can determine to answer the next query Applied Bionics and Biomechanics 7 Nodes Refers to Algorithm: A E A Old city begin. while (get next query) B Anarkali loop. C Lake road match the query with cache contents case D Lahore zoo if matched E Iqbal town B C D collect the output send request to server to pre-fetch next data else –if not matched Figure 7: Sample graph 2 for datasets. submit the query to DB server store the pre-fetch data to cache store query as dscriptor in cache Table 1: Data set with three types of locations. collect the output end-if Previous location Current location Next location end case (given) (given) (prediction) end loop AB C exit BC D end CD E Algorithm 1: Query processing using semantic cache. Table 2: Results of two graphs using Weka tool. A C DATASETS GRAPH #1 GRAPH #2 Dataset 1 68.80% 86.69% Nodes Refers to Dataset 2 76.00% 87.88% A Old city F D B Anarkali Dataset 3 77% Not available C Lake road D Lahore zoo Dataset 4 70.80% Not available E Iqbal town F Sadar bazar G Muslim town E G H H Gulshan-E-iqbal Table 1 has two attributes, i.e., previous, and current location. With the help of these, we will predict its next loca- Figure 6: Sample graph 1 for datasets. tion (class attribute). The actual dataset has 100 instances, which we used for training and testing of the Bayesian net- (iii) As soon as the user changes its location from A to work. Four datasets are presented for graph 1, while two any other one, the previous data will be no more are displayed for the graph in Figure 7. Following is an valid. According to the moving speed of the user, example of how it works in a Bayesian network. First, we will mobile D.B. regenerates the query for the new loca- compute the required probabilities from the given dataset. tion. The new query will again match with the cache Next, these probabilities help us predict the user’s next mov- descriptors to get the results ing location to C, given A and B. According to the predicted location, the following location data will be prefetched and (iv) If the prediction is accurate, a signal will be sent to saved in the cache. Table 2 depicts both graphs’ mean the server to notify it, where all the values of CPTs future-based probability with datasets. Thus, for mobile will be updated P2P services, we have observed that the range queries have shown incremental progress while performing the query 6. Discussion results [27]. Figure 6 represents two different graph locations, which use datasets in the Weka tool. At the same time, the similarity 7. Conclusion probabilities are shown in Figure 7. Figure 7 shows all locations, named A, B, C, D, and E, The processing of Location-Dependent Query (LDQ) pro- within the city. The arrows in the graph show the roads cessing has been more challenging considering the massive between two locations. If a user is at location A and moves increase in the usage patterns shifting massively towards from A to B, then we have its previous and current locations mobile clients. Hence, there is always a need for an efficient saved as a given probability, and the user’s next location may prediction algorithm that can determine accurate results be predicted, which can be either C or B. The probability of based on context. This will also provide better performance moving to location C is known as posterior probability, concerning time complexity for more extensive databases. which a Bayesian network will predict. For this purpose, this study focuses on predicting the future 8 Applied Bionics and Biomechanics [8] D. Thomas and S. M. Thampi, “Mobile query processing- based on the history of mobile users. 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