35913610
Knowledge Discovery Through Self-Organizing Maps: Data Visualization and Query Processing
Abstract. In data mining, the usefulness of a data pattern depends on the user of the database and does not solely depend on the statistical strength of the pattern. Based on the premise that heuristic search in combinatorial spaces built on computer and human cognitive theories is useful for effective knowledge discovery, this study investigates how the use of self-organizing maps as a tool of data visualization in data mining plays a significant role in human–computer interactive knowledge discovery. This article presents the conceptual foundations of the integration of data visualization and query processing for knowledge discovery, and proposes a set of query functions for the validation of self-organizing maps in data mining.
14662588
Bisimulation analysis of SDL-expressed protocols: a case study
Faster, better networks algorithms are often being discovered, and it is desirable to be able to replace an old algorithm by a new in a manner that is completely transparent to the application using it. This paper investigates the technique for ensuring such transparency for protocols expressed in SDL, via bisimulation checking. We discuss the main issues involved in translating SDL into Concurrency Workbench, a tool for performing bisimulation checking, and illustrate the feasibility of the technique by comparing the SDL specification of the Go-Back N protocol with the family of new protocols, called Asynchronous Retransmission Go-Back- N (AR). The latter perform better in environments characterized by high error rates and/or large propagation delays.
2866284
BLINKS: ranked keyword searches on graphs
Query processing over graph-structured data is enjoying a growing number of applications. A top-k keyword search query on a graph finds the top k answers according to some ranking criteria, where each answer is a substructure of the graph containing all query keywords. Current techniques for supporting such queries on general graphs suffer from several drawbacks, e.g., poor worst-case performance, not taking full advantage of indexes, and high memory requirements. To address these problems, we propose BLINKS, a bi-level indexing and query processing scheme for top-k keyword search on graphs. BLINKS follows a search strategy with provable performance bounds, while additionally exploiting a bi-level index for pruning and accelerating the search. To reduce the index space, BLINKS partitions a data graph into blocks: The bi-level index stores summary information at the block level to initiate and guide search among blocks, and more detailed information for each block to accelerate search within blocks. Our experiments show that BLINKS offers orders-of-magnitude performance improvement over existing approaches.
12580255
Comparison of Word and Subword Indexing Techniques for Mandarin Chinese Spoken Document Retrieval
In this paper, we investigate the use of words and subwords (including both characters and syllables) in audio indexing for Mandarin Chinese spoken document retrieval. Two retrieval approaches, including the well-known vector space model approach and the newly proposed HMM/Ngram-based approach, are used in the present work. We focus on the use of an entire Chinese textual story (from a newspaper) as a query to retrieve Mandarin Chinese spoken documents (from news broadcasts). Experiments are based on the Topic Detection and Tracking Corpora.
5874117
Mining concept-drifting data streams using ensemble classifiers
Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud protection, target marketing, network intrusion detection, etc. Conventional knowledge discovery tools are facing two challenges, the overwhelming volume of the streaming data, and the concept drifts. In this paper, we propose a general framework for mining concept-drifting data streams using weighted ensemble classifiers. We train an ensemble of classification models, such as C4.5, RIPPER, naive Beyesian, etc., from sequential chunks of the data stream. The classifiers in the ensemble are judiciously weighted based on their expected classification accuracy on the test data under the time-evolving environment. Thus, the ensemble approach improves both the efficiency in learning the model and the accuracy in performing classification. Our empirical study shows that the proposed methods have substantial advantage over single-classifier approaches in prediction accuracy, and the ensemble framework is effective for a variety of classification models.
7713940
ATLAS: A Small but Complete SQL Extension for Data Mining and Data Streams
Publisher Summary This chapter implements ATLAS, a powerful database language and system that enables users to develop complete data-intensive applications in structured query language (SQL)—by writing new aggregates and table functions in SQL, rather than in procedural languages as in current Object- Relational systems. As a result, ATLAS' SQL is Turing-complete, and is very suitable for advanced data intensive applications, such as data mining and stream queries. The ATLAS system is now available for download along with a suite of applications including various data mining functions that have been coded in ATLAS' SQL, and execute with a modest performance overhead with respect to the same applications written in C/C++. The demonstration reveals ATLAS' internal architecture by focusing on several of its key components, such as the query rewrite module, the query-plan generation module, and the stream-data management engine.
17778034
Evolutionary Gibbs sampler for image segmentation
We propose a novel evolutionary algorithm for the function optimization problem in Bayesian image segmentation with Markov random field prior. Function variables are partitioned into several codings. A pivot coding is selected and variables in it are evolved respectively according to their probability distributions which encode both the evolutionary pressure and contextual constraints from neighboring pixels. Variables in other codings are evolved according to their conditional probabilities. In summary, the algorithm is about building probabilistic models to guide search. It achieves the efficiency and flexibility by incorporating Gibbs sampler in an evolutionary approach. Remarkable performance is observed in some experiments.