35803772
Approximate range–sum query answering on data cubes with probabilistic guarantees
Approximate range aggregate queries are one of the most frequent and useful kinds of queries for Decision Support Systems (DSS), as they are widely used in many data analysis tasks. Traditionally, sampling-based techniques have been proposed to tackle this problem. However, their effectiveness degrade when the underlying data distribution is skewed. Another approach based on the outlier management can limit the effect of data skews but fails to address other requirements of approximate range aggregate queries, such as error guarantees and query processing efficiency. In this paper, we present a technique that provides approximate answers to range aggregate queries on OLAP data cubes efficiently, with theoretical guarantees on the errors. Our basic idea is to build different data structures to manage outliers and the rest of the data. Carefully chosen outliers are organized in a quad-tree based indexing data structure to provide efficient access for query processing. A query-workload adaptive, tree-like synopsis data structure, called TunablePartition-Tree (TP-Tree), is proposed to organize samples extracted from non-outlier data. Our experiments clearly demonstrate the merits of our technique, by comparing with previous well-known techniques.
11539721
XParent: an efficient RDBMS-Based XML database system
Presents, XParent, an XML document management system built on top of RDBMS. It is based on an efficient, model-mapping-based approach that uses a fixed database schema to store any XML documents without assistance of DTD. The visual query interface of XParent provides both expressive power for professionals and user friendliness for naive users. The proposed multi-level query translation scheme makes it possible to develop a generic XML application that supports multiple XML query languages and mapping schemas.
2266606
Space-efficient Relative Error Order Sketch over Data Streams
We consider the problem of continuously maintaining order sketches over data streams with a relative rank error guarantee ∊. Novel space-efficient and one-scan randomised techniques are developed. Our first randomised algorithm can guarantee such a relative error precision ∊ with confidence 1 - \delta using O( 1\_ \in \frac{1} {2}2 log 1d log ∊^2N) space, where N is the number of data elements seen so far in a data stream. Then, a new one-scan space compression technique is developed. Combined with the first randomised algorithm, the one-scan space compression technique yields another one-scan randomised algorithm that guarantees the space requirement is O( 1\frac{1} { \in } log(1\frac{1}{ \in } log 1\begin{gathered} \frac{1}{\delta } \hfill \\ \hfill \\ \end{gathered} )\frac{{\log ^{2 + \alpha } \in N}} {{1 - 1/2^\alpha }} (for\alpha \gt 0) on average while the worst case space remains O( \frac{1}{{ \in ^2 }}\log \frac{1} {\delta }\log \in ^2 N). These results are immediately applicable to approximately computing quantiles over data streams with a relative error guarantee \in and significantly improve the previous best space bound O( \frac{1} {{ \in ^3 }}\log \frac{1}{\delta }\log N). Our extensive experiment results demonstrate that both techniques can support an on-line computation against high speed data streams.
11973783
Answering Approximate Range Aggregate Queries on OLAP Data Cubes with Probabilistic Guarantees
Approximate range aggregate queries are one of the most frequent and useful kinds of queries for Decision Support Systems (DSS). Traditionally, sampling- based techniques have been proposed to tackle this problem. However, its effectiveness will degrade when the underlying data distribution is skewed. Another approach based on the outlier management can limit the effect of data skew but fails to address other requirements of approximate range aggregate queries, such as error guarantees and query processing efficiency. In this paper, we present a technique that provide approximate answers to range aggregate queries on OLAP data cubes efficiently with theoretical error guarantees. Our basic idea is to build different data structures for outliers and the rest of the data. Experimental results verified the effectiveness of our proposed methods.
1993887
Locating Motifs in Time-Series Data
Finding motifs in time-series is proposed to make clustering of time-series subsequences meaningful, because most existing algorithms of clustering time-series subsequences are reported meaningless in recent studies. The existing motif finding algorithms emphasize the efficiency at the expense of quality, in terms of the number of time-series subsequences in a motif and the total number of motifs found. In this paper, we formalize the problem as a continuous top-k motif balls problem in an m-dimensional space, and propose heuristic approaches that can significantly improve the quality of motifs with reasonable overhead, as shown in our experimental studies.
6564567
Stabbing the sky: efficient skyline computation over sliding windows
We consider the problem of efficiently computing the skyline against the most recent N elements in a data stream seen so far. Specifically, we study the n-of-N skyline queries; that is, computing the skyline for the most recent n (/spl forall/n/spl les/N) elements. Firstly, we developed an effective pruning technique to minimize the number of elements to be kept. It can be shown that on average storing only O(log/sup d/ N) elements from the most recent N elements is sufficient to support the precise computation of all n-of-N skyline queries in a d-dimension space if the data distribution on each dimension is independent. Then, a novel encoding scheme is proposed, together with efficient update techniques, for the stored elements, so that computing an n-of-N skyline query in a d-dimension space takes O(log N+s) time that is reduced to O(d log log N+s) if the data distribution is independent, where s is the number of skyline points. Thirdly, a novel trigger based technique is provided to process continuous n-of-N skyline queries with O(/spl delta/) time to update the current result per new data element and O(log s) time to update the trigger list per result change, where /spl delta/ is the number of element changes from the current result to the new result. Finally, we extend our techniques to computing the skyline against an arbitrary window in the most recent N element. Besides theoretical performance guarantees, our extensive experiments demonstrated that the new techniques can support on-line skyline query computation over very rapid data streams.
8284293
A note on list improper coloring planar graphs
A graph G is called (k, d)*-choosable if, for every list assignment L satisfying |L(v)| = k for all v ϵ V(G), there is an L-coloring of G such that each vertex of G has at most d neighbors colored with the same color as itself. In this note, we prove that every planar graph without 4-cycles and l-cycles for some l ϵ {5, 6, 7} is (3, 1)*-choosable.
10163897
Accelerating approximate subsequence search on large protein sequence databases
In this paper, we study the problem on how to build a persistent index structure for protein sequences to support approximate match. The suffix tree has been proposed as a solution to index sequence database and has been deployed on organizing DNA sequences (Hunt et al. (2001)). Unfortunately, it suffers from the problem of "memory bottleneck" that prevents it from being applied efficiently to a large database. The performance even degrades further for protein database due to a larger fanout at each node. Here, we employ an indexing structure, called BASS-tree, to support approximate match in sublinear time on a large protein database. We call this indexing method the sequence approximate match index method. The search of approximate matches can be properly directed to the portion in the database with a high potential of matching quickly. It is demonstrated in our experiments that the potential performance improvement is in an order of magnitude over alternative methods such as the BLAST algorithm and the suffix tree.
9757380
Algorithmic aspects of communication in ad-hoc networks with smart antennas
Smart antennas have gained significant importance in multi-hop wireless networks in recent years, because of their sophisticated signal processing capabilities that hold the potential for increased data rates and reliability. In this work, we consider the problem of communication in multi-hop wireless networks with smart antennas (specifically digital adaptive arrays). These smart antennas provide degrees of freedom (DOFs) that can be used to suppress co-existing communication links, thereby increasing spatial reuse in the network. Thus, the communication problem comprises of not just determining a channel access mechanism to be used by the communication links, but also involves the determination of the communication pattern (usage of DOFs) to be used by each node during channel access. To the best of our knowledge, our work is the first step towards addressing this problem.We first consider the problem of determining the communication pattern to be used by the nodes and formulate it combinatorially with the goal of optimizing network performance through interference minimization. We present efficient centralized and distributed algorithms that are within a factor of ¾ and ½ of the optimum solution respectively. We then extend the distributed algorithm to incorporate TDMA-based scheduling in a purely localized manner. The distributed algorithms are then evaluated through simulations in ns2 and insights are drawn into the potential performance benefits of smart antennas in multi-hop wireless networks.
10774024
Accurate Classification of Protein Structural Families Using Coherent Subgraph Analysis
Protein structural annotation and classification is an important problem in bioinformatics. We report on the development of an efficient subgraph mining technique and its application to finding characteristic substructural patterns within protein structural families. In our method, protein structures are represented by graphs where the nodes are residues and the edges connect residues found within certain distance from each other. Application of subgraph mining to proteins is challenging for a number reasons: (1) protein graphs are large and complex, (2) current protein databases are large and continue to grow rapidly, and (3) only a small fraction of the frequent subgraphs among the huge pool of all possible subgraphs could be significant in the context of protein classification. To address these challenges, we have developed an information theoretic model called coherent subgraph mining. From information theory, the entropy of a random variable X measures the information content carried by X and the Mutual Information (MI) between two random variables X and Y measures the correlation between X and Y. We define a subgraph X as coherent if it is strongly correlated with every sufficiently large sub-subgraph Y embedded in it. Based on the MI metric, we have designed a search scheme that only reports coherent subgraphs. To determine the significance of coherent protein subgraphs, we have conducted an experimental study in which all coherent subgraphs were identified in several protein structural families annotated in the SCOP database (Murzin et al, 1995). The Support Vector Machine algorithm was used to classify proteins from different families under the binary classification scheme. We find that this approach identifies spatial motifs unique to individual SCOP families and affords excellent discrimination between families.
7026910
An Approach to Active Spatial Data Mining Based on Statistical Information
Spatial data mining presents new challenges due to the large size of spatial data, the complexity of spatial data types, and the special nature of spatial access methods. Most research in this area has focused on efficient query processing of static data. This paper introduces an active spatial data mining approach that extends the current spatial data mining algorithms to efficiently support user-defined triggers on dynamically evolving spatial data. To exploit the locality of the effect of an update and the nature of spatial data, we employ a hierarchical structure with associated statistical information at the various levels of the hierarchy and decompose the user-defined trigger into a set of subtriggers associated with cells in the hierarchy. Updates are suspended in the hierarchy until their cumulative effect might cause the trigger to fire. It is shown that this approach achieves three orders of magnitude improvement over the naive approach that reevaluate the condition over the database for each update, while both approaches produce the same result without any delay. Moreover, this scheme can support incremental query processing as well.