1,721,015 research outputs found
CircularTrip and ArcTrip: effective grid access methods for continuous spatial queries.
Get PDFA k nearest neighbor query q retrieves k objects that lie closest to the query point q among a given set of objects P. With the availability of inexpensive location aware mobile devices, the continuous monitoring of such queries has gained lot of attention and many methods have been proposed for continuously monitoring the kNNs in highly dynamic environment. Multiple continuous queries require real-time results and both the objects and queries issue frequent location updates. Most popular spatial index, R-tree, is not suitable for continuous monitoring of these queries due to its inefficiency in handling frequent updates. Recently, the interest of database community has been shifting towards using grid-based index for continuous queries due to its simplicity and efficient update handling. For kNN queries, the order in which cells of the grid are accessed is very important. In this research, we present two efficient and effective grid access methods, CircularTrip and ArcTrip, that ensure that the number of cells visited for any continuous kNN query is minimum. Our extensive experimental study demonstrates that CircularTrip-based continuous kNN algorithm outperforms existing approaches in terms of both efficiency and space requirement. Moreover, we show that CircularTrip and ArcTrip can be used for many other variants of nearest neighbor queries like constrained nearest neighbor queries, farthest neighbor queries and (k + m)-NN queries. All the algorithms presented for these queries preserve the properties that they visit minimum number of cells for each query and the space requirement is low. Our proposed techniques are flexible and efficient and can be used to answer any query that is hybrid of above mentioned queries. For example, our algorithms can easily be used to efficiently monitor a (k + m) farthest neighbor query in a constrained region with the flexibility that the spatial conditions that constrain the region can be changed by the user at any time
Modeling Large Time Series for Efficient Approximate Query Processing
No full textEvolving customer requirements and increasing competition force business organizations to store increasing amounts of data and query them for information at any given time. Due to the current growth of data volumes, timely extraction of relevant information becomes more and more difficult with traditional methods. In addition, contemporary Decision Support Systems (DSS) favor faster approximations over slower exact results. Generally speaking, processes that require exchange of data become inefficient when connection bandwidth does not increase as fast as the volume of data. In order to tackle these issues, compression techniques have been introduced in many areas of data processing. In this paper, we outline a new system that does not query complete datasets but instead utilizes models to extract the requested information. For time series data we use Fourier and Cosine transformations and piece-wise aggregation to derive the models. These models are initially created from the original data and are kept in the database along with it. Subsequent queries are answered using the stored models rather than scanning and processing the original datasets. In order to support model query processing, we maintain query statistics derived from experiments and when running the system. Our approach can also reduce communication load by exchanging models instead of data. To allow seamless integration of model-based querying into traditional data warehouses, we introduce a SQL compatible query terminology. Our experiments show that querying models is up to 80% faster than querying over the raw data while retaining a high accuracy
Efficient processing of proximity based spatial queries
Get PDFSpatial databases play a vital role in many applications such as Geographic Information
Systems (GIS), Computer Aided Design (CAD), Very-Large-Scale Integration (VLSI)
designs, Multimedia Information System (MMIS), and medicine and biological research.
Due to their importance, a large body of work has focused on efficiently computing
various spatial queries. A proximity based spatial query computes the results based on
the proximity (closeness) between the objects. Range queries, reverse k nearest neighbors
(RkNN) queries and k-closest pairs queries are some of the most important and well
studied proximity based spatial queries. In this thesis, we provide efficient solutions for
these queries under various settings. Below is a brief description of our contributions.
We present several algorithms to answer RkNN queries under different settings. More
specifically, we present an approach, called Lazy Updates, to continuously monitor RkNN
queries in Euclidean space as well as in spatial networks. Lazy Updates outperforms all
of the existing techniques in terms of both the computation cost and the communication
cost. We devise another technique, based on a novel concept of influence zone, to efficiently
compute both snapshot and continuous RkNN queries. The influence zone based
approach outperforms the existing techniques for both the snapshot and the continuous
RkNN queries. We also provide efficient solution for probabilistic RNN queries for the
case when the underlying data is uncertain.
We are the first to study the efficient monitoring of moving range queries over a set
of static data objects in Euclidean space and in spatial networks. We conduct a rigorous
theoretical analysis to show the effectiveness of our approach. The theoretical results are
verified by an extensive experimental study. The experimental results also demonstrate
that the proposed approach is close to optimal.
We are the first to present a unified framework to answer a broad class of top-k pairs
queries including the k-closest pairs queries, k-furthest pairs queries and their bichromatic
variants. We conduct a rigorous complexity analysis and show that the expected cost of
our proposed algorithms is optimal when the scoring function uses at most two attributes
Adaptive Grid-Based k-median Clustering of Streaming Data with Accuracy Guarantee
No full textAbstract. Data stream clustering has wide applications, such as online financial transactions, telephone records, and network monitoring. Grid-based clustering partitions stream data into cells, derives statistical infor-mation of the cells, and then applies clustering on these much smaller sta-tistical information without referring to the input data. Therefore, grid-based clustering is efficient and very suitable for high-throughput data streams, which are continuous, time-varying, and possibly unpredictable. Various grid-based clustering schemes have been proposed. However, to the best of our knowledge, none of them provides an accuracy guarantee for their clustering output. To fill this gap, in this paper we study grid-based k-median clustering. We first develop an accuracy guarantee on the cost difference between grid-based solution and the optimum. Based on the theoretical analysis, we then propose a general and adaptive solu-tion, which partitions stream data into cells of dynamically determined granularity and runs k-median clustering on the statistical information of cells with an accuracy guarantee. An extensive experiment over three real datasets clearly shows that our algorithm provides high-quality clus-tering outputs in an efficient way.
A<sup>3</sup>SAR:Context-aware spatial augmented reality for anywhere, anyone, and analysis
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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