9428764
OLAP Query Processing Algorithm Based on Relational Storage
When the relational storage method is adopted in the data warehouse system, the most time-consuming operations in OLAP query are multitable join and group-by aggregation. Based on the characteristics of the data warehouse itself and the applications run on it, one new multi-table join algorithm -- Mjoin is given in this paper. And the performance of this new Mjoin algorithm is improved greatly, compared with other traditional multitable join algorithms. In this paper, a new sorting based group-by aggregation algorithm -- MuSA is also given, based on the Mjoin algorithm. The speed of sorting is remarkably improved in this new sorting based aggregation algorithm, as the keyword mapping technology is used to compress the sort keywords in the course of sorting.
8920901
Exploit Sequencing to Accelerate XML Twig Query Answering
Speeding up query evaluation in large XML repositories becomes a challenging and all-important problem with vast XML-related applications arising. In this paper, we present SCALER, an efficient algorithm for XML query answering based on UDFTS and effective twig structure matching scheme. UDFTS not only constructs a one-to-one correspondence between trees and sequences but also maintains critical parent-child relationships for twig structure matching. With SCALER, XML queries can be performed by subsequence matching without breaking twigs into sub paths and evaluating these paths individually. Thus, costly join operations can be avoided elegantly. We also show the correctness of query answering by eliminating false dismissals and false alarms naturally in SCALER. By a thorough experimental study on various real-life data, we prove the efficiency and scalability of SCALER over the previous known alternative.
32509112
A Highly Adaptable Web Information Extractor Using Graph Data Model
We present an approach to build highly adaptable extractor for collecting data from diverse Web sites. This approach uses Graph Model to represent content and structures as well as their various types of features. The generated graph is accompanied by a script in a special language called GQML containing the extraction rules. The running of the script transforms the graph into a specified format such as XML file that stores data from various Web sites in a uniform format. The experimental results show the presented approach is both effective and efficient.
15198141
Build Presentation Layer for Semantic Contents
Large scale of semantically enriched data is the foundation of the semantic web. We introduce the model used in SESQ system as the presentation layer of the semantic contents. It is an abstract graph independent of the data storage layer and application layer. Semantic contents of a specified domain are organized as nodes and arcs in the graph. GQML, a manipulation language, is designed for the graph, which is also used as the query language to semantic contents. With this model, the interoperation and integration of different sources will be easier. Now the model has been implemented on Berkley Database System and Relational Database.
28810751
Counting Graph Matches with Adaptive Statistics Collection
High performance of query processing in large scale graph-structured data poses a pressing demand for high-quality statistics collection and selectivity estimation. Precise and succinct statistics collection about graph-structured data plays a crucial role for graph query selectivity estimation. In this paper, we propose the approach SMT, Succinct Markov Table, which achieves high precision in selectivity estimation with low memory space consumed. Four core notions of SMT are constructing, refining, compressing and estimating. The efficient algorithm SMTBuilder provides facility to build adaptive statistics model in the form of SMT. Versatile optimization rules, which investigate local bi-directional reachability, are introduced in SMT refining. During compressing, affective SMT grouping techniques are introduced. Statistical methods are used for selectivity estimations of various graph queries basing on SMT, especially for twig queries. By a thorough experimental study, we demonstrate SMT's advantages in accuracy and space by comparing with previously known alternative, as well as the preferred optimization rules and compressing technique that would favor different real-life data.
14080806
Mining Complex Time-Series Data by Learning Markovian Models
In this paper, we propose a novel and general approach for time-series data mining. As an alternative to traditional ways of designing specific algorithm to mine certain kind of pattern directly from the data, our approach extracts the temporal structure of the time-series data by learning Markovian models, and then uses well established methods to efficiently mine a wide variety of patterns from the topology graph of the learned models. We consolidate the approach by explaining the use of some well-known Markovian models on mining several kinds of patterns. We then present a novel high-order hidden Markov model, the variable-length hidden Markov model (VLHMM), which combines the advantages of well- known Markovian models and has the superiority in both efficiency and accuracy. Therefore, it can mine a much wider variety of patterns than each of prior Markovian models. We demonstrate the power of VLHMM by mining four kinds of interesting patterns from 3D motion capture data, which is typical for the high-dimensionality and complex dynamics.
11780341
Sentential Association Based Text Classification Systems
We recently proposed a novel sentential association based approach SAT-MOD for text classification, which views a sentence rather than a document as an association transaction, and uses a novel heuristic called MODFIT to select the most significant itemsets for constructing a category classifier. Based on SAT-MOD, we have developed a prototype system called SAT-Class. In this demo, we demonstrate the effectiveness of our text classification system, and also the readability and refinability of acquired classification rules.
45051194
2-PS Based Associative Text Classification
Recent studies reveal that associative classification can achieve higher accuracy than traditional approaches. The main drawback of this approach is that it generates a huge number of rules, which makes it difficult to select a subset of rules for accurate classification. In this study, we propose a novel association-based approach especially suitable for text classification. The approach first builds a classifier through a 2-PS (Two-Phase) method. The first phase aims for pruning rules locally, i.e., rules mined within every category are pruned by a sentence-level constraint, and this makes the rules more semantically correlated and less redundant. In the second phase, all the remaining rules are compared and selected with a global view, i.e., training examples from different categories are merged together to evaluate these rules. Moreover, when labeling a new document, the multiple sentence-level appearances of a rule are taken into account. Experimental results on the well-known text corpora show that our method can achieve higher accuracy than many well-known methods. In addition, the performance study shows that our method is quite efficient in comparison with other classification methods.