Semantic Caching for Multiresolution Spatial Query Processing in Mobile Environments

Modern location-based applications rely extensively on the efficient processing of spatial data and queries. Spatial query engines are commonly engineered as an extension to a relational database or a cluster-computing framework. Large parts of the spatial processing runtime is spent on evaluating spatial predicates and traversing spatial indexing structures. Typical high-level implementations of these spatial structures incur significant interpretive overhead, which increases latency and lowers throughput. A promising idea to improve the performance of spatial workloads is to leverage native code generation techniques that have become popular in relational query engines. However, architecting a spatial query compiler is challenging since spatial processing has fundamentally different execution characteristics from relational workloads in terms of data dimensionality, indexing structures, and predicate evaluation. In this paper, we discuss the underlying reasons why standard query compilation techniques are not fully effective when applied to spatial workloads, and we demonstrate how a particular style of query compilation based on techniques borrowed from partial evaluation and generative programming manages to avoid most of these difficulties by extending the scope of custom code generation into the data structures layer. We extend the LB2 main-memory query compiler, a relational engine developed in this style, with spatial data types, predicates, indexing structures, and operators. We show that the spatial extension matches the performance of specialized library code and outperforms relational and map-reduce extensions.

Apache Flink is an open-source system for scalable processing of batch and streaming data. Flink does not natively support efficient processing of spatial data streams, which is a requirement of many applications dealing with spatial data. Besides Flink, other scalable spatial data processing platforms including GeoSpark, Spatial Hadoop, etc. do not support streaming workloads and can only handle static/batch workloads. To fill this gap, we present GeoFlink, which extends Apache Flink to support spatial data types, indexes and continuous queries over spatial data streams. To enable efficient processing of spatial continuous queries and for the effective data distribution across Flink cluster nodes, a gird-based index is introduced. GeoFlink currently supports spatial range, spatial kNN and spatial join queries on point data type. An experimental study on real spatial data streams shows that GeoFlink achieves significantly higher query throughput than ordinary Flink processing.

Efficient spatial query processing in Graph Database Management Systems (GDBMSs) has become increasingly important owing to the prevalence of spatial graph data. However, current GDBMSs lack effective spatial indexing, causing performance issues with complex spatial graph queries. This study proposes a spatial index called Subgraph Integrated R-Tree (SGIR-Tree) for efficient spatial query processing in GDBMSs. The SGIR-Tree integrates the hierarchical R-Tree structure with the graph structure of GDBMSs by converting R-Tree elements into graph components like nodes and edges. The Minimum Bounding Rectangle (MBR) information of spatial objects and R-Tree nodes is stored as properties of these graph elements, and the leaf nodes are directly connected to the spatial nodes. This approach combines the efficiency of spatial indexing with the flexibility of graph databases, thereby allowing spatial query results to be directly utilized in graph traversal. Experiments using OpenStreetMap datasets demonstrate that the SGIR-Tree outperforms the previous approaches in terms of query overhead and index overhead. The results are expected to improve spatial graph data processing in various fields, including location-based service and urban planning, significantly advancing spatial data management in GDBMSs.

Several applications of Wireless Sensor Networks (WSN) demand information only from sub-regions of the monitored area. The user establishes the region of interest (RoI) and requires the WSN to collect data only inside this region. This kind of query is called spatial query. The state of the art of spatial query processing considers, in general, that nodes do not fail. However, nodes can be destroyed, interference can deny the communication and nodes can go to sleep mode (turn off the radio in duty cycles) in order to save energy. This work proposes a fault-tolerant energy efficient in-network spatial query processing mechanism for WSN. The proposed mechanism employs failure detection algorithms in order to avoid failed neighbors when forwarding and processing spatial queries. Our contributions are lightweight and resilient because they operate on demand and avoid the use of routing tables.

Location-based services (LBSs) have entered into people’s daily lives with the evolution of telecommunication technologies. LBSs are the services that consider the locations of users to provide tailored information. We study a variety of location-based queries in urban environments. Mainly, we focus on processing such queries in the indoor space motivated by the abundance of applications of indoor LBSs and lack of existing studies supporting indoor spatial queries. Although the spatial query processing has been extensively studied for outdoor space, these techniques are not efficient when applied to indoor venues due to inherent differences in the topology of indoor space.

In this paper, we propose an efficient method of processing range spatial keyword queries. In particular, we focus on minimizing (i) the computation cost at the server and (ii) the communication cost between moving objects and the server. As the moving objects dynamically change their location, they should contact the server. This, however, increases the communication cost as well as the server computation cost, as the number of moving objects is increased. In the traditional researches, the concept of safe region is used. A safe region assigned to each moving object o is an area such that o cannot affect the results of the queries as long as it is moving within this area. However, the construction cost of the safe region can become a burden to the server. Therefore, we introduce pruning rules to decrease the overhead of constructing the safe region. In addition, the proposed algorithm for processing continuous range spatial keyword queries over moving objects utilizes a new spatial index structure, called the partition retrieved list, to help the system quickly construct the safe region of each object. Through experimental evaluations, we verify the effectiveness of our proposed algorithm.

In recent years, spatial applications have become more and more important in both scientific research and industry. Spatial query processing is the fundamental functioning component to support spatial applications. However, the state-of-the-art techniques of spatial query processing are facing significant challenges as the data expand and user accesses increase. In this paper we propose and implement a novel scheme (named VegaGiStore) to provide efficient spatial query processing over big spatial data and numerous concurrent user queries. Firstly, a geography-aware approach is proposed to organize spatial data in terms of geographic proximity, and this approach can achieve high aggregate I/O throughput. Secondly, in order to improve data retrieval efficiency, we design a two-tier distributed spatial index for efficient pruning of the search space. Thirdly, we propose an indexing + MapReduce'' data processing architecture to improve the computation capability of spatial query. Performance evaluations of the real-deployed VegaGiStore system confirm its effectiveness.

The broadcasting of spatial data together with an index structure is an effective way of disseminating data in a wireless mobile environment. Mobile clients requesting data tune into a continuous broadcast only when spatial data of interest and relevance is available on the channel and thus minimize their power consumption. A mobile client experiences latency (time elapsed from requesting to receiving data) and tuning time (the amount of time spent listening to the channel). This paper studies the execution of spatial queries on broadcasted tree-based spatial index structures. The focus is on queries that require a partial traversal of the spatial index, not only a single-path root-to-leaf search. We present techniques for processing spatial queries while mobile clients are listening to a broadcast of the tree. Our algorithms can handle clients with limited memory, trees broadcast with a certain degree of replication of index nodes, and algorithms executed at the clients may employ different data structures. Experimental work on R*-trees shows that these techniques lead to different tuning times and different latencies. Our solutions also lead to efficient methods for starting the execution of a query in the middle of a broadcast cycle. Spatial query processing in a multiple channel environment is also addressed.

Semantic cache enhances the capability of conventional (page/tuple) cache by adopting the dynamic strategy to group the contents and semantics of already processed queries. Query processing and cache management are two major activities for semantic caching. Semantic caching demands efficient, correct and complete algorithms to process incoming queries. Efficiency of query processing activity in semantic cache mainly depends on query trimming. Query trimming process consists of two sub processes; query matching and query splitting. Efficient query matching and query splitting process will ensures the optimal query trimming process. In this paper, we have proposed query trimming algorithm by defining query matching algorithm and using query splitting algorithm from the literature. To optimize the query matching process we have proposed graph based semantic indexing scheme. On the basis of proposed indexing scheme we have designed query matching algorithm (qTrim). We have proved that complexity of graph based query matching algorithm is reduced to linear from exponential. Comparison of graph based (proposed) scheme is done with segment based scheme. On the basis of comparison and runtime complexity we have argued that the proposed algorithm is an optimum to trim the queries into sub-queries (probe and remainder).

Caching is important for any system attempting to achieve high performance. The semantic caching is an approach trying to benefit from the certain knowledge of data semantics. The authors expect that this information might enable reuse of semantically close data rather than exactly equal to cached data in the traditional system. However, the major obstacle for extensive application of semantic caching for any data model or query language is the computational complexity of the query containment problem, which is, in general, undecidable. In this article the authors introduce and compare three approximate conservative query matching algorithms for semantic caching of semi-structured queries. The authors then analyze their applicability for distributed query processing. Based on this analysis, the authors outline few scenarios where semantic caching can be beneficial for query processing in a distributed system of heterogeneous semi-structured information resources.

Spatial database research has continued to advance greatly in the last three decades addressing the growing data management and analysis needs of spatial applications. This research has produced a taxonomy of models for space, conceptual models, spatial query languages and query processing, spatial file organization and indexes, and spatial data mining. However, emerging needs for spatial database systems include the handling of 3‐D spatial data, temporal dimension with spatial data, and spatial data visualization. In addition, the rise of new systems such as sensor networks and multicore processors is likely to have an impact in spatial databases. The goal of this article is to provide a broad overview of the recent advancements in spatial databases and of research needs in each area.

Context-aware and quality-aware algorithms for efficient mobile object management

With advances in wireless networks and hand-held computing devices equipped with location sensing capability (e.g., PDAs, laptops, and smart phones), a large number of location based services (LBSs) have been successfully deployed. In LBSs, wireless broadcast is an efficient method to support the large number of users. In wireless broadcast environment, existing research proposed to support range queries search, may tune into unnecessary indexes or data object. This paper addresses the problem of processing range queries on wireless broadcast streams. In order to support range queries efficiently, we propose a novel indexing scheme called Distributed Space-Partitioning Index (DSPI). DSPI consists of hierarchical grids that provide mobile clients with the global view as well as the local view of the broadcast data. The algorithm for processing range queries based on DSPI is also proposed. Simulation experiments demonstrate DSPI is superior to the existing index schemes.

POIs (points of interest) as well as users’ check-in information and their ratings on POIs have been widely studies in systems providing location-based services. We note that users usually have their own preferences for POI categories and their own network of friends. Therefore, we aim to provide for a group of users a new kind of POI-finding query that considers not only POI preferences of each user but also other aspects of location-based social networks such as users’ locations and POI ratings. We name such a new query as Spatial Group Preference (SGP) query. For a group of users, an SGP query returns top-k POIs that are much likely to satisfy the needs of users. Specially, we propose a new evaluation model that considers user preferences for user preferences for POI categories. Based on this model, we develop basic algorithms based on R-tree to evaluate SGP queries. Further, we design a new index structure called CR-tree to accelerate the query performance. We prove that CR-tree has better pruning efficiency than the traditional R-tree. We conduct experiments on a simulation dataset as well as two real datasets with respect to various configurations. The results suggest the efficiency of our proposal.

The emerging new services for GPS and mobile users have developed applications that access and exploit spatial objects with new components (e.g. text and social network). Web objects, including blogs, tweets, photos and videos, are embedded into a map by the APIs of map service providers, where textual messages are associated with geographic information. Location-based social networking services, arising from Facebook and Foursquare, allow users to browse and share their traces of locations among the social networks. Among this class of applications, the highlight is that the retrieved spatial objects (e.g. points of interest and moving users) are featured with new components. Integrating such new components into the spatial query processing has produced large amounts of promising results. However, handling new components along with the retrieval of spatial objects increases the complexity of such joint query processing significantly. Thus, management over data from such multiple domains has been received considerable attention from database research community.\n\nIn this thesis, we introduce three interesting problems and study their sophisticated solutions for processing spatial objects with new components: (i) category-aware optimal route query (CORQ), (ii) social and spatial ranking query (SSRQ), and (iii) efficient notification of meeting point (ENMP) query. Our results for (i) and (ii) facilitate the retrieval of spatial objects from multiple domains while our solutions for (iii) provide effective tools for synchronous management of multiple moving users from a social network. Category-aware optimal route queries (CORQ) are generalized from the traveling salesman problem and enable users to retrieve shortest routes covering selected categories. Social and spatial ranking queries (SSRQ) are relevant to spatial object recommendations using social information and allow users to obtain the spatial objects that not only are near their locations but also impress them with high social influence. Efficient notification of meeting point (ENMP) queries are variants of aggregate nearest neighbor queries and provide real-time rearrangement for multiple moving users according to their locations.\n\nQuery processing in such multiple domains is complicated due to the mixture of domain information and their integration within one search. Naïve algorithms for these problems incur either numerous expensive evaluations or massive communication cost, which render them inapplicable to large datasets. Our main research purpose is to design efficient and effective solutions for the proposed problems, that avoid the aforementioned shortcomings of naïve algorithms.