Window Queries Research Articles

A Spatial Indexing Scheme for Location Based Service Queries in a Single Wireless Broadcast Channel

The rapid advancement of mobile devices and wireless network technology has accelerated the development of applications in the mobile computing field. In particular, LBS (Location-Based Services) are one of the fastest growing areas in mobile applications. In a wireless data broadcasting environment, a server can effectively provide LBS to a large population of mobile clients. The two most typical types of LBS queries are window and kNN (k-Nearest Neighbors) queries. Previously proposed window and kNN query processing schemes in a single wireless broadcast channel environment tend to access much unnecessary index information and data objects due to their low filtering powers. In this paper, we propose a new spatial index called HMI (Hilbert curve-based MBR filtering Index) for the efficient processing of window and kNN queries in a single wireless broadcast channel. HMI is a tree-structured index whose construction is based on the Hilbert curve and MBR (Minimum Bounding Rectangle). By combining an MBR structure with the Hilbert curve order, HMI obtains the advantages of both structures. We present an in-depth experimental analysis of our method by comparing it with current existing schemes. Our performance analysis shows that HMI significantly decreases the average access and tuning times of kNN queries over current existing schemes. HMI also gives better average tuning times and comparable average access times of window queries over current existing schemes.

무선 공간 데이터 방송 환경에서 범위 질의 처리를 위한 시뮬레이션 프레임워크의 설계와 구현

무선 공간 데이터 방송 환경에서 범위 질의 처리를 위한 시뮬레이션 프레임워크의 설계와 구현

Towards a Painless Index for Spatial Objects

Conventional spatial indexes, represented by the R-tree, employ multidimensional tree structures that are complicated and require enormous efforts to implement in a full-fledged database management system (DBMS). An alternative approach for supporting spatial queries is mapping-based indexing, which maps both data and queries into a one-dimensional space such that data can be indexed and queries can be processed through a one-dimensional indexing structure such as the B + . Mapping-based indexing requires implementing only a few mapping functions, incurring much less effort in implementation compared to conventional spatial index structures. Yet, a major concern about using mapping-based indexes is their lower efficiency than conventional tree structures. In this article, we propose a mapping-based spatial indexing scheme called Size Separation Indexing (SSI). SSI is equipped with a suite of techniques including size separation, data distribution transformation, and more efficient mapping algorithms. These techniques overcome the drawbacks of existing mapping-based indexes and significantly improve the efficiency of query processing. We show through extensive experiments that, for window queries on spatial objects with nonzero extents , SSI has two orders of magnitude better performance than existing mapping-based indexes and competitive performance to the R-tree as a standalone implementation. We have also implemented SSI on top of two off-the-shelf DBMSs, PostgreSQL and a commercial platform, both having R-tree implementation. In this case, SSI is up to two orders of magnitude faster than their provided spatial indexes. Therefore, we achieve a spatial index more efficient than the R-tree in a DBMS implementation that is at the same time easy to implement. This result may upset a common perception that has existed for a long time in this area that the R-tree is the best choice for indexing spatial objects.

Spatial-temporal Indexing Research based on Road Network: Improved-MON-tree

Contrary to MON-tree index less than the temporal and spatial indexing methods based on road networks we proposed Improved-MON-tree. The index is divided into two levels and the quad tree grid index at the top of the road network, and the bottom of the two-dimensional Rtree index moving object moving information. In order to support the global trajectory of moving objects query, add a hash structure underlying index structure connected to the moving object information. Verified by experiment, under certain conditions, ImprovedMON-tree than the MON-tree based on edge and routes, has good spatial-temporal window query performance and global trajectory.

A distributed air index based on maximum boundary rectangle over grid-cells for wireless non-flat spatial data broadcast.

In the pervasive computing environment using smart devices equipped with various sensors, a wireless data broadcasting system for spatial data items is a natural way to efficiently provide a location dependent information service, regardless of the number of clients. A non-flat wireless broadcast system can support the clients in accessing quickly their preferred data items by disseminating the preferred data items more frequently than regular data on the wireless channel. To efficiently support the processing of spatial window queries in a non-flat wireless data broadcasting system, we propose a distributed air index based on a maximum boundary rectangle (MaxBR) over grid-cells (abbreviated DAIM), which uses MaxBRs for filtering out hot data items on the wireless channel. Unlike the existing index that repeats regular data items in close proximity to hot items at same frequency as hot data items in a broadcast cycle, DAIM makes it possible to repeat only hot data items in a cycle and reduces the length of the broadcast cycle. Consequently, DAIM helps the clients access the desired items quickly, improves the access time, and reduces energy consumption. In addition, a MaxBR helps the clients decide whether they have to access regular data items or not. Simulation studies show the proposed DAIM outperforms existing schemes with respect to the access time and energy consumption.

Direct neighbor search

In this paper we study a novel query type, called direct neighbor query. Two objects in a dataset are direct neighbors (DNs) if a window selection may exclusively retrieve these two objects. Given a source object, a DN search computes all of its direct neighbors in the dataset. The DNs define a new type of affinity that differs from existing formulations (e.g., nearest neighbors, nearest surrounders, reverse nearest neighbors, etc.) and finds application in domains where user interests are expressed in the form of windows, i.e., multi-attribute range selections. Drawing on key properties of the DN relationship, we develop an I/O optimal processing algorithm for data indexed with a spatial access method. In addition to plain DN search, we also study its K-DN and all-DN variants. The former relaxes the DN condition – two objects are K-DNs if a window query may retrieve them and only up to K−1 other objects – whereas the all-DN variant computes the DNs of every object in the dataset. Using real, large-scale data, we demonstrate the efficiency and practicality of our approach, and show that it vastly outperforms a competitor constructed from previous work.

Window Query and Analysis on Massive Spatio-temporal Data

Along with the expansion of computer-based climate simulations, efficient visualization and analysis of massive climate data are becoming more important than ever. In this paper, we try to explore the factors behide climate changes by combining window query and time-varying data mining techniques. With constant query time and acceptable storage cost, the algorithms presented support various queries on 3d time-varying datasets, such as average, min, and max value. A new time-varying data analysis algorithm is given, which is especially suitable for analyzing big data. All these algorithms have been implemented on and integrated into a visual analysis system, with tiled-LCD ultra-resolution display. Experimental results on several datasets from practical applications are presented.

A Review of Window Query Processing for Data Streams

In recent years, progress in hardware technology has resulted in the possibility of monitoring many events in real time. The volume of incoming data may be so large, that monitoring all individual data might be intractable. Revisiting any particular record can also be impossible in this environment. Therefore, many database schemes, such as aggregation, join, frequent pattern mining, and indexing, become more challenging in this context. This paper surveys the previous efforts to resolve these issues in processing data streams. The emphasis is on specifying and processing sliding window queries, which are supported in many stream processing engines. We also review the related work on stream query processing, including synopsis structures, plan sharing, operator scheduling, load shedding, and disorder control.

Efficient duality-based subsequent matching on time-series data in green computing

Green computing is the study and practice of efficiently using computers resources. The main purpose of green computing is to achieve an algorithmic efficiency by designing resource-efficient, accurate and energy-efficient algorithms. It is important to achieve the algorithmic efficiency in handling time-series data. One of the main tasks in handling time-series data is to find subsequence matches similar to a given query sequence. The state-of-the-art methods to find subsequence matches in time-series data produce many false alarms by filtering points through comparing only one query window with its corresponding data window. In this paper, we propose a subsequence matching method for green computing, which is called the Efficient Duality-based Subsequence Matching (simply, E-Dual Match). E-Dual Match handles all possible query windows for determining candidates. Hence, E-Dual Match not only reduces the false alarms, and improves the performance compared to Dual Match, but also does so by considering the main requirements of the green computing. In other words, E-Dual Match efficiently uses limited computer resources, accurate and energy-efficient. Experiment results show that E-Dual Match reduces the number of candidates by up to 4.90 times over Dual Match, and improves the subsequence matching time by up to 2.35 times over Dual Match. We also show that E-Dual Match reduces the number of data page accesses by up to 3.04 times over Dual Match.

The BoND-Tree: An Efficient Indexing Method for Box Queries in Nonordered Discrete Data Spaces

Box queries (or window queries) are a type of query which specifies a set of allowed values in each dimension. Indexing feature vectors in the multidimensional Nonordered Discrete Data Spaces (NDDS) for efficient box queries are becoming increasingly important in many application domains such as genome sequence databases. Most of the existing work in this field targets the similarity queries (range queries and k-NN queries). Box queries, however, are fundamentally different from similarity queries. Hence, the same indexing schemes designed for similarity queries may not be efficient for box queries. In this paper, we present a new indexing structure specifically designed for box queries in the NDDS. Unique characteristics of the NDDS are exploited to develop new node splitting heuristics. For the BoND-tree, we also provide theoretical analysis to show the optimality of the proposed heuristics. Extensive experiments with synthetic data demonstrate that the proposed scheme is significantly more efficient than the existing ones when applied to support box queries in NDDSs. We also show effectiveness of the proposed scheme in a real-world application of primer design for genome sequence databases.

Mobile Big Data Query Based on Double R-Tree and Double Indexing

The amount of data in our industry and the world is exploding. Data is being collected and stored at unprecedented rates. The challenge is not only to store and manage the vast volume of data, which is also called big data, but also to analyze and query from it. In order to put forward the universal method to response mobile big data query, queries are separated and grouped according to kinds of query for massive mobile objects in the space. The indexing method for grouping the mobile objects with Grid (GG TPR-tree) has great efficiency to manage a massive capacity of mobile objects within a limited area, but it only could meet a part of requirements for mobile big data query if the GG TPR-tree was used solely. This thesis offers solutions to simple immediate query, simple continuous query, active window query, and continuous window query, dynamic condition query and other query requests by employing DTDI index structure. The experiments prove that with the support of DTDI index structure, query of massive mobile objects has higher precision and better query performance.

A Fast RFID Identification Protocol with Low Tag Complexity

In this letter, a novel Query window Tree (QwT) protocol is proposed and presented. Based on the Query Tree (QT), a fast anti-collision protocol with low tag complexity (which preserves the memoryless feature of the QT) is achieved. The QwT effectively controls the number of responded bits from tags by adopting a dynamic-size `window'. As a result, the tags of the proposed QwT deliver exclusively the bits that are required by the window controlled at the reader, rather than sending their full ID. Simulations show that the QwT positively decreases the total number of bits that are transmitted by a tag and as a result mitigates latency.

Influence of Managing the Number of Tag Bits Transmitted on the Query Tree RFID Collision Resolution Protocol

Radio Frequency Identification (RFID) technology is increasingly becoming popular, for its widespread use and more sophisticated applications. The coexistence of tags sharing the communication channel requires solutions to message collisions, which degrade bandwidth, and increase the number of transmitted bits. A new methodology called ‘window’ is presented to manage the number of bits transmitted by a tag. The aim is show how the query tree (QT) protocol is influenced by this feature, and how the performance of the novel protocol, query window tree (QwT), improves when the tag ID distribution is correlated. Therefore, we have performed a fair comparison of the Query Tree and the new proposed QwT protocol for various tag ID distributions. Simulations show that the QwT positively decreases the total number of bits that are transmitted by tags.

A Proxy-Based Approach to Continuous Location-Based Spatial Queries in Mobile Environments

Caching valid regions of spatial queries at mobile clients is effective in reducing the number of queries submitted by mobile clients and query load on the server. However, mobile clients suffer from longer waiting time for the server to compute valid regions. We propose in this paper a proxy-based approach to continuous nearest-neighbor (NN) and window queries. The proxy creates estimated valid regions (EVRs) for mobile clients by exploiting spatial and temporal locality of spatial queries. For NN queries, we devise two new algorithms to accelerate EVR growth, leading the proxy to build effective EVRs even when the cache size is small. On the other hand, we propose to represent the EVRs of window queries in the form of vectors, called estimated window vectors (EWVs), to achieve larger estimated valid regions. This novel representation and the associated creation algorithm result in more effective EVRs of window queries. In addition, due to the distinct characteristics, we use separate index structures, namely EVR-tree and grid index, for NN queries and window queries, respectively. To further increase efficiency, we develop algorithms to exploit the results of NN queries to aid grid index growth, benefiting EWV creation of window queries. Similarly, the grid index is utilized to support NN query answering and EVR updating. We conduct several experiments for performance evaluation. The experimental results show that the proposed approach significantly outperforms the existing proxy-based approaches.

Generic cumulative annular bucket histogram for spatial selectivity estimation of spatial database management system

Selectivity estimation is crucial to query optimizers in choosing an optimal execution plan in a given spatial query, and there has been a great deal of focus on how to achieve good selectivity estimation for finer spatial selection operators. Equally crucial to this is understanding how to produce an updated spatial histogram. With this in mind, we used a cumulative annular bucket histogram (AB histogram), which not only accurately estimates the selectivity of a spatial selection or a spatial join operation with finer operators but also provides an updated spatial histogram to estimate the selectivity of subsequent spatial operations in a multi-level spatial query plan. A basic unit of AB histogram stores the number of minimum bounding rectangles whose lower left points and upper right points are located in specific rectangular regions. According to the basic units of a cumulative AB histogram, we can find out the selectivity of a spatial selection with a number of different finer operators. When it comes to spatial join operations, a relationship between two cumulative AB histograms can be translated into a relationship between one histogram and numerous query windows from the other histogram. Furthermore, an updated cumulative AB histogram can be simultaneously built into the process of selectivity calculation, making it possible to achieve both selectivity and an updated histogram of spatial join; its implementation made in the optimizer facility (OPF) of INGRES9.2. To highlight the performance of a cumulative AB histogram, several experiments have been conducted, with results showing that the cumulative AB histogram not only supports the selectivity estimation of spatial selection and spatial join with ‘Disjoint’, ‘Intersect’, ‘Within’, ‘Contains’, ‘Crosses’ and ‘Overlap’ operators but also supports the generation of an updated histogram. This indicates that Ingres would do better to find a query plan with low-execution costs.

Exploiting compression and approximation paradigms for effective and efficient online analytical processing over sensor network readings in data grid environments

SUMMARYAggregate queries are useful tools in the context of sensor network‐based systems as they retrieve knowledge from huge amounts of summarized readings to be exploited for knowledge discovery purposes. Actually, data representation and query models are problematic issues for managing sensor network data, because streams produced by sensors are theoretically unbounded. In this paper, we present a Grid framework, called SensorGrid, on the basis of data compression and approximation paradigms, which allows us to provide approximate answers to aggregate queries on summarized sensor network data. These queries are the basis for achieving Online Analytical Processing (OLAP) over sensor network readings in Data Grid environments, with both effectiveness and efficiency. We also present our experience in the context of a real‐life system focused on the management of environmental sensor network data. Another contribution of our research is represented by the extensive experimental evaluation and analysis of SensorGrid, which, in more details, focuses on two main classes of aggregate range queries over sensor readings, namely, (i) the window queries, which apply an SQL aggregation operator over a fixed window over the reading stream produced by the sensor network, and (ii) the continuous queries, which instead consider a ‘moving’ window and produce as output a stream of answers. Both classes of queries are extremely useful to extract summarized knowledge to be exploited by OLAP‐like analysis tools over sensor network data. The experimental results, conducted on both synthetic and real‐life data sets, clearly confirm the benefits deriving from embedding data compression and approximation paradigms into Grid‐based sensor network data‐intensive management systems.Copyright © 2013 John Wiley & Sons, Ltd.

Neighbor-Index Method for Continuous Window Queries over Wireless Data Broadcast

A continuous window query is an important class of spatial queries for location-based services. It retrieves spatial objects in a fixed window region of every point on a line segment and indicates the valid segments of them. In this paper, we focus on continuous window queries in wireless data broadcast systems. Since the query result of the continuous window queries has the spatial locality, providing neighbor information of spatial objects can guide clients to efficiently retrieve related objects. Therefore, we propose a neighbor-index method to efficiently support the continuous window queries in wireless data broadcast systems. The proposed method interleaves the neighbor information between spatial objects to guide mobile clients to quickly retrieve the answered objects and save the power consumption of the mobile devices. Experimental results show that our method outperforms the distributed indexing.

MLAIN: Multi-leveled air indexing scheme in non-flat wireless data broadcast for efficient window query processing

In ubiquitous computing, it is critical to allow a great number of clients to access information simultaneously at any place and at any time. Wireless data broadcasting provides effective information services due to its own high scalability. In this paper, we propose a novel indexing scheme for spatial data items that adopts multi-leveled grid partition to support window queries in non-flat data broadcasting that considers clients’ skewed data access patterns. In the proposed scheme, each cell of the partition is restricted to its number of data items. This constraint makes the proposed scheme different from other schemes that use a space partition. Cell indexes of cells that keep link information between cells are interleaved with data items on the channel. The scheme allows clients to access queried data items quickly by reducing the broadcast cycle and the spacing between indexes, and providing multiple paths to a cell on the channel. We show the efficiency of the proposed scheme with regard to the access time, tuning time, and energy consumption using intensive simulation studies.

A high-performance web-based information system for publishing large-scale species range maps in support of biodiversity studies

Functionality, performance and scalability are critical to Web-based information systems for publishing and disseminating large-scale species distribution data. Existing systems do not support dynamic spatial window queries on large-scale species range maps that are important to compute alpha and beta diversities for biodiversity analysis and modeling. In this study, we have developed a main-memory based novel quadtree data structure to represent large-scale species range maps and support dynamic spatial window queries to retrieve a list of species and their area sizes within a query window efficiently. Using the NatureServe's 4000+ bird species range maps, experiment results have shown that the memory footprint of the proposed quadtree data structure representing the range maps of all the species is about 1/6 of the quadtree derived by combining individual quadtrees each representing a species range map. The experiment results have also demonstrated that the query response times of our main-memory spatial database are well below a fraction of a second for query windows as large as 10×10°, which are 2–3 orders better than using a typical disk-resident spatial database system.

Complex window query support for monitoring streaming data in wireless body area networks

Wireless Body Area Networks (WBAN) combined with various body sensors and personal electronic devices are emerging as a new platform for medical applications. This paper proposes a real-time monitoring system with complex window query support for enabling advanced monitoring on streaming data that is produced by the body sensors and transmitted through the WBAN. The main techniques developed are the caching for multi-source data stream support, complex range expression processing with interval skip lists, and historic data processing for sliding window condition monitoring support. The condition monitoring query language and efficient processing mechanisms to support the complex monitoring are presented. Experimental results show that the proposed approach provides a wider spectrum of monitoring functions as well as enhancing the scalability and efficiency of the monitoring.