Window Queries Research Articles

A Hybrid Approach for Data Warehouse View Selection

Materialized view selection is one of the crucial decisions in designing a data warehouse for optimal efficiency. Static selection of views may materialize certain views that are not beneficial as the data and usage trends change over time. On the contrary, dynamic selection of views works better only for queries demanding a high degree of aggregation. These facts point to the need for a technique that combines the improved response time of the static approach and the automated tuning capability of the dynamic approach. In this article, we propose a hybrid approach for the selection of materialized views. The idea is to partition the collection of all views into a static and dynamic set such that views selected for materialization from the static set are persistent over multiple query (and maintenance) windows, whereas views selected from the dynamic set can be queried and/or replaced on the fly. Highly aggregated views are selected on the fly based on the query access patterns of users, whereas the more detailed static set of views plays a significant role in the efficient maintenance of the dynamic set of views and in answering certain detailed view queries. We prove that our proposed strategy satisfies the monotonicity requirements, which is essential in order for the greedy heuristic to deliver competitive solutions. Experimental results show that our approach outperforms Dynamat, a well-known dynamic view management system that is known to outperform optimal static view selection.

Exploiting predicate-window semantics over data streams

The continuous sliding-window query model is used widely in data stream management systems where the focus of a continuous query is limited to a set of the most recent tuples. In this paper, we show that an interesting and important class of queries over data streams cannot be answered using the sliding-window query model. Thus, we introduce a new model for continuous window queries, termed the predicate-window query model that limits the focus of a continuous query to the stream tuples that qualify a certain predicate. Predicate-window queries have some distinguishing characteristics, e.g., (1) The window predicate can be defined over any attribute in the stream tuple (ordered or unordered). (2) Stream tuples qualify and disqualify the window predicate in an out-of-order manner. In this paper, we discuss the applicability of the predicate-window query model. We will show how the existing sliding-window query models fail to answer some of the predicate-window queries. Finally, we discuss the challenges in supporting the predicate-window query model in data stream management systems.

A granularity-aware parallel aggregation method for data streams

This paper focuses on the parallel aggregation processing of data streams based on the shared-nothing architecture. A novel granularity-aware parallel aggregating model is proposed. It employs parallel sampling and linear regression to describe the characteristics of the data quantity in the query window in order to determine the partition granularity of tuples, and utilizes equal depth histogram to implement partitioning. This method can avoid data skew and reduce communication cost. The experiment results on both synthetic data and actual data prove that the proposed method is efficient, practical and suitable for time-varying data streams processing.

On past-time indexing of moving objects

Tracking of mobile objects trajectories is one of many modern applications supported by Spatiotemporal databases. Within the context of this application, queries about the present, future or past positions of the objects need to be answered. Several indexing methods have been proposed to efficiently handle such spatiotemporal queries. In the current paper, we propose a method for indexing the historic (past) positions of moving objects called XBR-tree, a quadtree-like technique that is able to handle both timestamp and window queries. Moreover, we compare experimentally this with other methods proposed in the literature for the same purpose. In particular, we compare XBR-trees with PMR-trees, structures also related to quadtrees and MV3R-trees, R-tree based structures.

Continuous Query Processing of Spatio-Temporal Data Streams in PLACE

The tremendous increase in the use of cellular phones, GPS-like devices, and RFIDs results in highly dynamic environments where objects as well as queries are continuously moving. In this paper, we present a continuous query processor designed specifically for highly dynamic environments (e.g., location-aware environments). We implemented the proposed continuous query processor inside the PLACE server (Pervasive Location-Aware Computing Environments); a scalable location-aware database server developed at Purdue University. The PLACE server extends data streaming management systems to support location-aware environments. These environments are characterized by the wide variety of continuous spatio-temporal queries and the unbounded spatio-temporal streams. The proposed continuous query processor includes: (1) New incremental spatio-temporal operators to support a wide variety of continuous spatio-temporal queries, (2) Extended semantics of sliding window queries to deal with spatial sliding windows as well as temporal sliding windows, and (3) A shared-execution framework for scalable execution of a set of concurrent continuous spatio-temporal queries. Experimental evaluation shows promising performance of the continuous query processor of the PLACE server.

A new range query algorithm for Universal B-trees

In multi-dimensional databases the essential tool for accessing data is the range query (or window query). In this paper we introduce a new algorithm of processing range query in universal B-tree (UB-tree), which is an index structure for searching in multi-dimensional databases. The new range query algorithm (called the DRU algorithm) works efficiently, even for processing high-dimensional databases. In particular, using the DRU algorithm many of the UB-tree inner nodes need not to be accessed. We explain the DRU algorithm using a simple geometric model, providing a clear insight into the problem. More specifically, the model exploits an interesting relation between the Z-curve and generalized quad-trees. We also present experimental results for the DRU algorithm implementation.

Spatial Queries in Wireless Broadcast Systems

Owing to the advent of wireless networking and personal digital devices, information systems in the era of mobile computing are expected to be able to handle a tremendous amount of traffic and service requests from the users. Wireless data broadcast, thanks to its high scalability, is particularly suitable for meeting such a challenge. Indexing techniques have been developed for wireless data broadcast systems in order to conserve the scarce power resources in mobile clients. However, most of the previous studies do not take into account the impact of location information of users. In this paper, we address the issues of supporting spatial queries (including window queries and kNN queries) of location-dependent information via wireless data broadcast. A linear index structure based on the Hilbert curve and corresponding search algorithms are proposed to answer spatial queries on air. Experiments are conducted to evaluate the performance of the proposed indexing technique. Results show that the proposed index and its enhancement outperform existing algorithms significantly.

일반화된 누적밀도 히스토그램을 이용한 공간 선택율 추정

누적밀도 히스토그램은 사각형 객체의 네 점에 대응하는 4개의 서브 히스토그램을 유지함으로써 사각형 객체가 여러 버켓에 걸쳐질 경우 발생하는 다중 계산 문제를 해결하고 있다. 이 기법은 빠른 추정시간과 정확한 결과를 제공하고 있지만, 질의 윈도우가 그리드 셀의 경계와 일치해야 한다는 제약사항을 기반으로 수행하므로, 실제 응용에 적용시 많은 에러를 초래하게 된다. 따라서, 이 논문에서는 기존 누적밀도 히스토그램에서 질의 윈도우의 제약사항에 관한 영향을 줄이기 위해, 두가지 확률모델을 기반으로 일반화된 누적밀도 히스토그램을 사용한 선택율 추정 기법을 제안하였다. 제안된 두가지 확률 모델은 \circled1질의 영역 비율을 고려한 확률모델과, \circled2교차 영역 정보를 고려한 확률모델이다. 우리는 실제 데이터 셋을 사용하여 제안된 기법을 실험하였다 실험 결과는 이 논문에서 제안된 기법이 기존의 다른 선택율 추정 기법보다 성능이 뛰어남을 보여주고 있다 더구나, 교차 영역 정보를 기반으로 하는 확률모델의 경우 20% 질의 윈도우에서 5% 미만의 낮은 에러율을 보였다. 이 논문에서 제안된 기법은 사각형 객체의 공간 범위 질의의 선택율을 정확하게 추정하는데 사용될 수 있다. Multiple-count problem is occurred when rectangle objects span across several buckets. The CD histogram is a technique which selves this problem by keeping four sub-histograms corresponding to the four points of rectangle. Although It provides exact results with constant response time, there is still a considerable issue. Since it is based on a query window which aligns with a given grid, a number of errors nay be occurred when it is applied to real applications. In this paper, we propose selectivity estimation techniques using the generalized cumulative density histogram based on two probabilistic models : \circled1 probabilistic model which considers the query window area ratio, \circled2 probabilistic model which considers intersection area between a given grid and objects. Our method has the capability of eliminating an impact of the restriction on query window which the existing cumulative density histogram has. We experimented with real datasets to evaluate the proposed methods. Experimental results show that the proposed technique is superior to the existing selectivity estimation techniques. Furthermore, selectivity estimation technique based on probabilistic model considering the intersection area is very accurate(less than 5% errors) at 20% query window. The proposed techniques can be used to accurately quantify the selectivity of the spatial range query on rectangle objects.

고정 그리드 인덱싱에서 공간과 시간 필터링을 이용한 범위 질의 처리

최근 들어 이동 통신 분야에서 이동하는 고객을 위한 위치 기반 서비스가 중요한 서비스로 부각되고 있다. 이동객체 응용의 경우 갱신 연산이 많고, 부하가 특정 지역에 집중되는 특징이 있다. 윈도우 나 원형 형태의 범위질의는 위치 기반 서비스에서 중요한 질의 중 하나이다. 이러한 범위질의에서는 부분 포함된 셀과 완전 포함된 셀을 구분해야할 필요가 있다. 또한 올바른 한정된 객체를 골라내기 위해 시간 영역을 고려할 필요성이 있다. 본 논문에서는 갱신연산을 최소화하기 위해 고안되어진 2번째 단계에 고정 그리드 구조를 적용한 2단계 인덱스 구조를 적용한다. VP 필터링과 윈도우 셀 필터링 기법을 이용한 공간 셀 필터링 기법과 Time Zone 개념을 사용하여 시공간 개념이 결합된 필터링 기법을 제안한다. 제안된 방법의 성능 측정을 위해서 다른 필터링 조합을 가지고 다양한 윈도우 질의와 원 질의에 대해서 실험 결과를 보인다. Recently, the location-based service for moving customers is becoming one of the most important service in mobile communication area. For moving object applications, there are lots of update operations and such update loads are concentrated on some particular area unevenly. Range queries, whose range can be window or circular, are the most essential query types in LBS. We need to distinguish completely contained cells from partially contained cells in those range queries. Also, it is necessary to consider temporal dimension to filter out qualifying objects correctly. In this paper, we adopt two-level index structures with fixed grid file structures in the second level, which are designed to minimize update operations. We propose a spatial ceil filtering method using VP filtering and a combined spatio-temporal filtering method using time gone concepts. Some experimental results are shown for various window queries and circular queries with different filtering combinations to show the performance tradeoffs of the proposed methods.

Querying high-dimensional data in single-dimensional space

In this paper, we propose a new tunable index scheme, called iMinMax($\theta$), that maps points in high-dimensional spaces to single-dimensional values determined by their maximum or minimum values among all dimensions. By varying the tuning “knob”, $\theta$, we can obtain different families of iMinMax structures that are optimized for different distributions of data sets. The transformed data can then be indexed using existing single-dimensional indexing structures such as the B+-trees. Queries in the high-dimensional space have to be transformed into queries in the single-dimensional space and evaluated there. We present efficient algorithms for evaluating window queries as range queries on the single-dimensional space. We conducted an extensive performance study to evaluate the effectiveness of the proposed schemes. Our results show that iMinMax($\theta$) outperforms existing techniques, including the Pyramid scheme and VA-file, by a wide margin. We then describe how iMinMax could be used in approximate K-nearest neighbor (KNN) search, and we present a comparative study against the recently proposed iDistance, a specialized KNN indexing method.

A strip-splitting-based optimal algorithm for decomposing a query window into maximal quadtree blocks

Decomposing a query window into maximal quadtree blocks is a fundamental problem in quadtree-based spatial database. Recently, Proietti presented the first optimal algorithm for solving this problem. Given a query window of size n/sub 1//spl times/n/sub 2/, Proietti's algorithm takes O(n/sub 1/) time, where n/sub 1/=max(n/sub 1/, n/sub 2/). Based on a strip-splitting approach, we present a new optimal algorithm for solving the same problem. Experimental results reveal that our proposed algorithm is quite competitive with Proietti's algorithm.

Conditioned taste aversion: a database

The present commentary describes the availability of a database on conditioned taste aversion learning, the avoidance of fluids and foods previously associated with the aversive effects of a variety of drugs. The database includes articles as early as 1955 by Garcia and his colleagues [Science 122 (1955) 127] (reporting such avoidance in rats) and papers just published given that the database is ongoing and constantly updated. At the printing of this announcement, approximately 2600 papers are included in the database. The database allows the user to search for articles by author(s), key word(s), date, title and journal. These terms can be used as single entries or multiple combinations. Finally, the full database can be viewed by performing the search function without entering any terms in the query window. The database can be accessed at http://www.CTALearning.com.

Analysis of predictive spatio-temporal queries

Given a set of objects S , a spatio-temporal window query q retrieves the objects of S that will intersect the window during the (future) interval q T . A nearest neighbor query q retrieves the objects of S closest to q during q T . Given a threshold d , a spatio-temporal join retrieves the pairs of objects from two datasets that will come within distance d from each other during q T . In this article, we present probabilistic cost models that estimate the selectivity of spatio-temporal window queries and joins, and the expected distance between a query and its nearest neighbor(s). Our models capture any query/object mobility combination (moving queries, moving objects or both) and any data type (points and rectangles) in arbitrary dimensionality. In addition, we develop specialized spatio-temporal histograms, which take into account both location and velocity information, and can be incrementally maintained. Extensive performance evaluation verifies that the proposed techniques produce highly accurate estimation on both uniform and non-uniform data.

Estimating the result size of a query to velocity skewed moving objects

With new developments in positioning systems and electronics, various researches for moving objects have progressed [3,5,6]. Our paper is related to selection queries considering future positions of moving objects, which are referred to as future queries [5]. An example of the future query is as follows: “which airplanes will be inside a query window 20 minutes from now?” Airplanes correspond to moving objects that move as time passes. Recently, a data model that can deal with the future locations of an object has been proposed [5]. Like various studies [2,4,6], our work is based on this model.

Spatial queries in dynamic environments

Conventional spatial queries are usually meaningless in dynamic environments since their results may be invalidated as soon as the query or data objects move. In this paper we formulate two novel query types, time parameterized and continuous queries , applicable in such environments. A time-parameterized query retrieves the actual result at the time when the query is issued, the expiry time of the result given the current motion of the query and database objects, and the change that causes the expiration. A continuous query retrieves tuples of the form < result , interval >, where each result is accompanied by a future interval , during which it is valid. We study time-parameterized and continuous versions of the most common spatial queries (i.e., window queries, nearest neighbors, spatial joins), proposing efficient processing algorithms and accurate cost models.

Toward an accurate analysis of range queries on spatial data

Analysis of range queries on spatial (multidimensional) data is both important and challenging. Most previous analysis attempts have made certain simplifying assumptions about the data sets and/or queries to keep the analysis tractable. As a result, they may not be universally applicable. This paper proposes a set of five analysis techniques to estimate the selectivity and number of index nodes accessed in serving a range query. The underlying philosophy behind these techniques is to maintain an auxiliary data structure, called a density file, whose creation is a one-time cost, which can be quickly consulted when the query is given. The schemes differ in what information is kept in the density file, how it is maintained, and how this information is looked up. It is shown that one of the proposed schemes, called cumulative density (CD), gives very accurate results (usually less than 5 percent error) using a diverse suite of point and rectangular data sets, that are uniform or skewed, and a wide range of query window parameters. The estimation takes a constant amount of time, which is typically lower than 1 percent of the time that it would take to execute the query, regardless of data set or query window parameters.

A generalized comparison of linear representations of thematic layers

In this paper, the efficient encoding and manipulation of large sets of two-dimensional data that represent multiple non-overlapping features is investigated. Both linear structures are examined, i.e., leafcode and treecode representations of thematic layers. A new technique is presented and evaluated by applying it to already proposed access methods that are based on the previous representations. More specifically, we experiment with window queries that involve multiple features having as a performance measure the number of disk accesses. Experimentally it is shown that treecode representations outperform the previous structures with respect to time and space complexity.

Window Query Processing in Linear Quadtrees

The linear quadtree is a spatial access method that is built by decomposing the spatial objects in a database into quadtree blocks and storing these quadtree blocks in a B-tree. The linear quadtree is very useful for geographic information systems because it provides good query performance while using existing B-tree implementations. An algorithm and a cost model are presented for processing window queries in linear quadtrees. The algorithm can handle query windows of any shape in the general case of spatial databases with overlapping objects. The algorithm recursively decomposes the space into quadtree blocks, and uses the quadtree blocks overlapping the query window to search the B-tree. The cost model estimates the I/O cost of processing window queries using the algorithm. The cost model is also based on a recursive decomposition of the space, and it uses very simple parameters that can easily be maintained in the database catalog. Experiments with real and synthetic data sets verify the accuracy of the cost model.

An efficient spatial access method for spatial images containing multiple non-overlapping features

In this paper we propose and analyze a new spatial access method, namely the S*-tree, for the efficient secondary memory encoding and manipulation of images containing multiple non-overlapping features (i.e., coloured images). The S*-tree is based on a non-straightforward and space efficient extension to coloured images of its precursor, namely the S +-tree, which was explicitly designed for binary images. To assess experimentally the qualities of the S*-tree, we test it against the HL-quadtree, a previous spatial access method for coloured images, which is known to be space and time efficient. Our experiments show that the S*-tree reaches up to a 75% of space saving, and performs constantly less I/O accesses than the HL-quadtree in solving classical window queries.

An image compression method for spatial search

The maintenance of large raster images under spatial operations is still a major performance bottleneck. For reasons of storage space, images in a collection, such as satellite pictures in geographic information systems, are maintained in compressed form. Instead of performing a spatially selective operation on an image by first decompressing the compressed version, we propose to perform queries directly on the compressed version of the image. We suggest a compression technique that allows for the subsequent use of a spatial index structure to guide a spatial search. In response to a window query, our algorithm delivers a compressed partial image, or the exact uncompressed requested image region. In addition to the support of spatial queries on compressed continuous tone images, the new compression algorithm is even competitive in terms of the compression ratio that it achieves, compared to other standard lossless compression techniques.