9669585
Conceptual construction on incomplete survey data
The raw survey data for data mining are often incomplete. The issues of missing data in knowledge discovery are often ignored in data mining. This article presents the conceptual foundations of data mining with incomplete survey data, and proposes query processing for knowledge discovery and a set of query functions for the conceptual construction in survey data mining. Through a case, this paper demonstrates that conceptual construction on incomplete data can be accomplished by using artificial intelligence tools such as self-organizing maps.
38802324
Experiments with improved approximate mean value analysis algorithms
Abstract Approximate mean value analysis (MVA) is a popular technique for analyzing queueing networks because of the efficiency and accuracy that it affords. In this paper, we present a new software package, called the improved approximate mean value analysis library (IAMVAL) , which can be easily integrated into existing commercial and research queueing network analysis packages. The IAMVAL packages include two new approximate MVA algorithms, the queue line (QL) algorithm and the fraction line (FL) algorithm, for analyzing multiple class separable queueing networks. The QL algorithm is always more accurate than, and yet has approximately the same computational efficiency as, the Bard–Schweitzer proportional estimation (PE) algorithm, which is currently the most widely used approximate MVA algorithm. The FL algorithm has the same computational cost and, in noncongested separable queueing networks where queue lengths are quite small, yields more accurate solutions than both the QL and PE algorithms.
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.
7772067
Extractive Chinese Spoken Document Summarization Using Probabilistic Ranking Models
The purpose of extractive summarization is to automatically select indicative sentences, passages, or paragraphs from an original document according to a certain target summarization ratio, and then sequence them to form a concise summary. In this paper, in contrast to conventional approaches, our objective is to deal with the extractive summarization problem under a probabilistic modeling framework. We investigate the use of the hidden Markov model (HMM) for spoken document summarization, in which each sentence of a spoken document is treated as an HMM for generating the document, and the sentences are ranked and selected according to their likelihoods. In addition, the relevance model (RM) of each sentence, estimated from a contemporary text collection, is integrated with the HMM model to improve the representation of the sentence model. The experiments were performed on Chinese broadcast news compiled in Taiwan. The proposed approach achieves noticeable performance gains over conventional summarization approaches.
5495725
User-directed exploration of mining space with multiple attributes
There has been a growing interest in mining frequent itemsets in relational data with multiple attributes. A key step in this approach is to select a set of attributes that group data into transactions and a separate set of attributes that labels data into items. Unsupervised and unrestricted mining, however is stymied by the combinatorial complexity and the quantity of patterns as the number of attributes grows. In this paper we focus on leveraging the semantics of the underlying data for mining frequent itemsets. For instance, there are usually taxonomies in the data schema and functional dependencies among the attributes. Domain knowledge and user preferences often have the potential to significantly reduce the exponentially growing mining space. These observations motivate the design of a user-directed data mining framework that allows such domain knowledge to guide the mining process and control the mining strategy. We show examples of tremendous reduction in computation by using domain knowledge in mining relational data with multiple attributes.
72839
Distributing streaming media content using cooperative networking
In this paper, we discuss the problem of distributing streaming media content, both live and on-demand, to a large number of hosts in a scalable way. Our work is set in the context of the traditional client-server framework. Specifically, we consider the problem that arises when the server is overwhelmed by the volume of requests from its clients. As a solution, we propose Cooperative Networking (CoopNet), where clients cooperate to distribute content, thereby alleviating the load on the server. We discuss the proposed solution in some detail, pointing out the interesting research issues that arise, and present a preliminary evaluation using traces gathered at a busy news site during the flash crowd that occurred on September 11, 2001.
10504856
ViST: a dynamic index method for querying XML data by tree structures
With the growing importance of XML in data exchange, much research has been done in providing flexible query facilities to extract data from structured XML documents. In this paper, we propose ViST, a novel index structure for searching XML documents. By representing both XML documents and XML queries in structure-encoded sequences, we show that querying XML data is equivalent to finding subsequence matches. Unlike index methods that disassemble a query into multiple sub-queries, and then join the results of these sub-queries to provide the final answers, ViST uses tree structures as the basic unit of query to avoid expensive join operations. Furthermore, ViST provides a unified index on both content and structure of the XML documents, hence it has a performance advantage over methods indexing either just content or structure. ViST supports dynamic index update, and it relies solely on B+ Trees without using any specialized data structures that are not well supported by DBMSs. Our experiments show that ViST is effective, scalable, and efficient in supporting structural queries.
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.