Jong P. Yoon

BitCube: A Three-Dimensional Bitmap Indexing for XML Documents

XML is a new standard for exchanging and representing information on the Internet. Documents can be hierarchically represented by XML-elements. In this paper, we propose that an XML document collection be represented and indexed using a bitmap indexing technique. We define the similarity and popularity operations suitable for bitmap indexes. We also define statistical measurements in the BitCube: center, and radius. Based on these measurements, we describe a new bitmap indexing based technique to cluster XML documents. The techniques for clustering are motivated by the fact that the bitmap indexes are expected to be very sparse.Furthermore, a 2-dimensional bitmap index is extended to a 3-dimensional bitmap index, called the BitCube. Sophisticated querying of XML document collections can be performed using primitive operations such as slice, project, and dice. Experiments show that the BitCube can be created efficiently and the primitive operations can be performed more efficiently with the BitCube than with other alternatives.

A framework for knowledge discovery and evolution in databases

A concept for knowledge discovery and evolution in databases is described. The key issues include: using a database query to discover new rules; using not only positive examples (answer to a query), but also negative examples to discover new rules; and harmonizing existing rules with the new rules. A tool for characterizing the exceptions in databases and evolving knowledge as a database evolves is developed. >

Intelligent network management: a heterogeneous knowledge source approach

The knowledge and data engineering aspects of modeling the structural and functional components of a modern telecommunications network are presented. The networks are highly intelligent and dynamic, with the capability to reroute traffic between switches automatically and to support a customers virtual network. The goal of this modeling is to automatically identify and isolate network faults as manifested by alarms given off by network elements. The authors posit the need for several knowledge/data sources that cooperate in this process: (1) the network topology and connectivity model, (2) the alarm monitoring model, and (3) the rule and case base to provide high-level problem-solving guidance to isolate primary alarms (faults) from their triggered secondary alarms. Models (1) and (2) are developed in detail, and it is shown that they can be merged to facilitate the correlation of alarm events to network elements. Both models are specified using the knowledge entity relationship model.<<ETX>>

Data and information architectures for large-scale distributed data intensive information systems

The Earth Observing System (EOS) Data and Information System (EOSDIS) is perhaps one of the most important examples of large-scale, geographically distributed, and data intensive systems. The paper presents various facets of a data and information architecture for EOSDIS. EOS data is organized by means of an object-oriented schema, while EOS knowledge is organized through multiple domain-specific thesauri, complemented by domain knowledge and rules. The information holdings are organized into the source data archives, a data warehouse which provides an integrated view of the information holdings, and information marts which generate value-added information products for specialized user communities. Finally a federated client-server architecture is proposed to allow non-EOSDIS systems to become members of the EOSDIS community, allowing them to access EOSDIS holdings, and sharing their own data with EOSDIS.

BitCube: a three-dimensional bitmap indexing for XML documents

We describe a new bitmap indexing based technique to cluster XML documents. XML is a new standard for exchanging and representing information on the Internet. Documents can be hierarchically represented by XML-elements. XML documents are represented and indexed using a bitmap indexing technique. We define the similarity and popularity operations available in bitmap indexes and propose a method for partitioning a XML document set. Furthermore, a 2-dimensional bitmap index is extended to a 3-dimensional bitmap index, called BitCube. We define statistical measurements in the BitCube: mean, mode, standard derivation, and correlation coefficient. Based on these measurements, we also define the slice, project, and dice operations on a BitCube. BitCube can be manipulated efficiently and improves the performance of document retrieval.

XSSmon: A Perl based IDS for the detection of potential XSS attacks

Recent years have seen an explosion in the number of cross site scripting (XSS) incidents effecting Web sites and Web applications. As such, an intrusion detection system (IDS) capable of detecting potential cross site scripting attacks is demonstrated. The IDS involves the capturing of potential client side executable content on a Web page and the hashing of that content. At a future point in time, the Web page is reprocessed for client side executable content and the content rehashed, with any differences in the hash values indicative of a potential XSS attack. It is believed that the described IDS technique would be particularly useful for Web forums and other user content driven site, since the IDS only considers content recognized as potentially executable and not normal text content, such as that which would be typically enclosed in paragraph or heading tags.

Trend similarity and prediction in time-series databases

Many algorithms for discovering similar patterns from time- series databases involve three phases: First, sequential data in time domain is transformed into frequency domain using DFT. Then, the first few data points are considered to depict in an R*-tree. Those points in an R*-tree are compared by their distance. Any pair of data points, if the distance between them is within a certain threshold, are found to be similar. This approach results in performance problem due to emphasis on each data point itself. This paper proposes a novel method of finding similar trend patterns, rather than similar data patterns, from time-series database. As opposed to similar data patterns in the frequency domain, a limited number of points, in the time series, that play a dominant role to make a movement direction are taken into account. Those data points are called a trend sequence. Trend sequences will be defined in various ways. Of many, we focus more on considering trend sequences by a data smoothing technique. We know that a trend sequence contains far fewer data points than an original data sequence, but entails abstract level of sequence movements. To some extent, given a trend sequence, we apply the smoothing algorithm to predict the very next trend data. It is likely that once a trend sequence is found, the very next trend data point is expected. This paper also shows a method for trend prediction. We observed that our approach presented in this paper can be applied to finding similarity among many large time-series data sequences to the prediction of next possible data points to follow.

Managing faults in telecommunications networks: a taxonomy to knowledge-based approaches

A taxonomy is presented of knowledge-based approaches to fault management for telecommunications networks. Fault management systems are first characterized as multistage process systems. Using this characterization, a discussion is presented of a variety of current systems reported in the open literature that implement automated fault management and which span all processing stages (from alarm receipt to fault correction). The authors then focus on the diagnostic stage of each system and categorize the methods in terms of the major artificial intelligence methods employed. They conclude by considering an apparent trend in automated fault management systems toward an increased use of semantic knowledge and other approaches to describe network behavior. The emphasis throughout is on the use of information technology for automating fault management, rather than on engineering-level details of any specific subsystem.<<ETX>>

Semantic query optimization in deductive object-oriented databases

This paper addresses the problem of semantic query reformulation in the context of object-oriented deductive databases. It extends the declarative object-oriented specifications of F-logic proposed by Kifer and Lausen using the semantic query optimization technique developed by Chakravarthy, Grant, and Minker. In general, query processing in object-oriented databases is expensive when a query incorporates declarative rules, methods and inherited properties. We introduce the technique of semantic query reformulation for F-logic queries which transforms the original query into an equivalent, semantically-rich query that is more efficiently processed. We also discuss the issues of conflict resolution strategies and query evaluation priorities for queries involving the upper bounds of objects in the F-logic “type” lattice.

Semantic Update Optimization in Active Databases

In an active database, an update may be constrained by integrity constraints, and may also trigger rules that, in turn, may affect the database state. The general problem is to effect the update while also managing the “side-effects” of constraint enforcement and rule execution. In this paper an update calculus is proposed by which updates, constraints and rules are specified and managed within the same formalism. Constraints and production rules are expressed in a constraint language based on first-order logic. These logic expressions are used to semantically transform an original update into a sequence of updates that reflect the relevant constraints and production rules. The inference mechanism associated with processing a reformulated query ensures that: 1) the pre- and post-conditions of an update are satisfied, 2) update side-effects are propagated, and 3) repairs are made to tuples exhibiting constraint violations. Thus, a user-specified “update” is transformed, through semantic reformulation techniques, into a sequence of updates which together ensure semantic integrity of the original update as well as its propagated side-effects.