Wesley W. Chu

Optimal File Allocation in a Multiple Computer System

A model is developed for allocating information files required in common by several computers. The model considers storage cost, transmission cost, file lengths, and request rates, as well as updating rates of files, the maximum allowable expected access times to files at each computer, and the storage capacity of each computer. The criterion of optimality is minimal overall operating costs (storage and transmission). The model is formulated into a nonlinear integer zero-one programming problem, which may be reduced to a linear zero-one programming problem. A simple example is given to illustrate the model.

An index-based approach for similarity search supporting time warping in large sequence databases

This paper proposes a new novel method for similarity search that supports time warping in large sequence databases. Time warping enables finding sequences with similar patterns even when they are of different lengths. Previous methods for processing similarity search that supports time warping fail to employ multi-dimensional indexes without false dismissal since the time warping distance does not satisfy the triangular inequality. Our primary goal is to innovate on search performance without permitting any false dismissal. To attain this goal, we devise a new distance function D/sub tw-lb/ that consistently underestimates the time warping distance and also satisfies the triangular inequality D/sub tw-lb/ uses a 4-tuple feature vector that is extracted from each sequence and is invariant to time warping. For efficient processing of similarity search, we employ a multi-dimensional index that uses the 4-tuple feature vector as indexing attributes and D/sub tw-lb/ as a distance function. The extensive experimental results reveal that our method achieves significant speedup up to 43 times with real-world S&P 500 stock data and up to 720 times with very large synthetic data.

Comparative analysis of six XML schema languages

As XML [5] is emerging as the data format of the internet era, there is an substantial increase of the amount of data in XML format. To better describe such XML data structures and constraints, several XML schema languages have been proposed. In this paper, we present a comparative analysis of six noteworthy XML schema languages.

CoBase: a scalable and extensible cooperative information system

A new generation of information systems that integrates knowledge base technology with database systems is presented for providing cooperative (approximate, conceptual, and associative) query answering. Based on the database schema and application characteristics, data are organized into Type Abstraction Hierarchies (TAHs). The higher levels of the hierarchy provide a more abstract data representation than the lower levels. Generalization (moving up in the hierarchy), specialization (moving down the hierarchy), and association (moving between hierarchies) are the three key operations in deriving cooperative query answers for the user. Based on the context, the TAHs can be constructed automatically from databases. An intelligent dictionary/directory in the system lists the location and characteristics (e.g., context and user type) of the TAHs. CoBase also has a relaxation manager to provide control for query relaxations. In addition, an explanation system is included to describe the relaxation and association processes and to provide the quality of the relaxed answers. CoBase uses a mediator architecture to provide scalability and extensibility. Each cooperative module, such as relaxation, association, explanation, and TAH management, is implemented as a mediator. Further, an intelligent directory mediator is provided to direct mediator requests to the appropriate service mediators. Mediators communicate with each other via KQML. The GUI includes a map server which allows users to specify queries graphically and incrementally on the map, greatly improving querying capabilities. CoBase has been demonstrated to answer imprecise queries for transportation and logistic planning applications. Currently, we are applying the CoBase methodology to match medical image (X-ray, MRI) features and approximate matching of emitter signals in electronic warfare applications.

A Social Network-Based Recommender System (SNRS)

Social influence plays an important role in product marketing. However, it has rarely been considered in traditional recommender systems. In this chapter, we present a new paradigm of recommender systems which can utilize information in social networks, including user preferences, item’s general acceptance, and influence from social friends. A probabilistic model is developed to make personalized recommendations from such information. We extract data from a real online social network, and our analysis of this large data set reveals that friends have a tendency to select the same items and give similar ratings. Experimental results on this data set show that our proposed system not only improves the prediction accuracy of recommender systems but also remedies the data sparsity and cold-start issues inherent in collaborative filtering. Furthermore, we propose to improve the performance of our system by applying semantic filtering of social networks and validate its improvement via a class project experiment. In this experiment we demonstrate how relevant friends can be selected for inference based on the semantics of friend relationships and finer-grained user ratings. Such technologies can be deployed by most content providers.

Knowledge-based image retrieval with spatial and temporal constructs

A knowledge-based approach to retrieve medical images by feature and content with spatial and temporal constructs is developed. Selected objects of interest in an image are segmented and contours are generated. Features and content are extracted and stored in a database. Knowledge about image features can be expressed as a type abstraction hierarchy (TAH), the high-level nodes of which represent the most general concepts. Traversing TAH nodes allows approximate matching by feature and content if an exact match is not available. TAHs can be generated automatically by clustering algorithms based on feature values in the databases and hence are scalable to large collections of image features. Since TAHs are generated based on user classes and applications, they are context- and user-sensitive. A knowledge-based semantic image model is proposed to represent the various aspects of an image objects characteristics. The model provides a mechanism for accessing and processing spatial, evolutionary and temporal queries. A knowledge-based spatial temporal query language (KSTL) has been developed that extends ODMGs OQL and supports approximate matching of features and content, conceptual terms and temporal logic predicates. Further, a visual query language has been developed that accepts point-click-and-drag visual iconic input on the screen that is then translated into KSTL. User models are introduced to provide default parameter values for specifying query conditions. We have implemented the KMeD (Knowledge-based Medical Database) system using these concepts.

Conceptual Modeling – ER 2004

The envisioned Semantic Web aims to provide richly annotated and explicitly structured Web pages in XML, RDF, or description logics, based upon underlying ontologies and thesauri. Ideally, this should enable a wealth of query processing and semantic reasoning capabilities using XQuery and logical inference engines. However, we believe that the diversity and uncertainty of terminologies and schema-like annotations will make precise querying on a Web scale extremely elusive if not hopeless, and the same argument holds for large-scale dynamic federations of Deep Web sources. Therefore, ontology-based reasoning and querying needs to be enhanced by statistical means, leading to relevanceranked lists as query results. This paper presents steps towards such a “statistically semantic” Web and outlines technical challenges. We discuss how statistically quantified ontological relations can be exploited in XML retrieval, how statistics can help in making Web-scale search efficient, and how statistical information extracted from users’ query logs and click streams can be leveraged for better search result ranking. We believe these are decisive issues for improving the quality of next-generation search engines for intranets, digital libraries, and the Web, and they are crucial also for peer-to-peer collaborative Web search. 1 The Challenge of “Semantic” Information Search The age of information explosion poses tremendous challenges regarding the intelligent organization of data and the effective search of relevant information in business and industry (e.g., market analyses, logistic chains), society (e.g., health care), and virtually all sciences that are more and more data-driven (e.g., gene expression data analyses and other areas of bioinformatics). The problems arise in intranets of large organizations, in federations of digital libraries and other information sources, and in the most humongous and amorphous of all data collections, the World Wide Web and its underlying numerous databases that reside behind portal pages. The Web bears the potential of being the world’s largest encyclopedia and knowledge base, but we are very far from being able to exploit this potential. Database-system and search-engine technologies provide support for organizing and querying information; but all too often they require excessive manual preprocessing, such as designing a schema and cleaning raw data or manually classifying documents into a taxonomy for a good Web portal, or manual postprocessing such as browsing through large result lists with too many irrelevant items or surfing in the vicinity of promising but not truly satisfactory approximate matches. The following are a few example queries where current Web and intranet search engines fall short or where data P. Atzeni et al. (Eds.): ER 2004, LNCS 3288, pp. 3–17, 2004. c

Efficient searches for similar subsequences of different lengths in sequence databases

We propose an indexing technique for fast retrieval of similar subsequences using time warping distances. A time warping distance is a more suitable similarity measure than the Euclidean distance in many applications, where sequences may be of different lengths or different sampling rates. Our indexing technique uses a disk-based suffix tree as an index structure and employs lower-bound distance functions to filter out dissimilar subsequences without false dismissals. To make the index structure compact and thus accelerate the query processing, we convert sequences of continuous values to sequences of discrete values via a categorization method and store only a subset of suffixes whose first values are different from their preceding values. The experimental results reveal that our proposed technique can be a few orders of magnitude faster than sequential scanning.

Constraints-preserving transformation from XML document type deffinition to relational schema

As Extensible Markup Language (XML) [5] is emerging as the data format of the internet era, there are increasing needs to effciently store and query XML data. One way towards this goal is using relational database by transforming XML data into relational format. In this paper, we argue that existing transformation algorithms are not complete in the sense that they focus only on structural aspects and ignoring semantic aspects. We present the semantic knowledge that needs to be captured during the transformation to ensure a correct relational schema. Further, we show a simple algorithm that can 1) derive such semantic knowledge from the given XML Document Type Definition (DTD) and 2) preserve the knowledge by representing them in terms of semantic constraints in relational database terms. By combining the existing transformation algorithms and our constraints-preserving algorithm, one can transform XML DTD to relational schema where correct semantics and behaviors are guaranteed by the preserved constraints. Experimental results are also presented.

On the Analysis and Modeling of a Class of Computer Communication Systems

Recent advances in computer communications are discussed including computer-traffic and channel error characteristics, optimal fixed message block size, statistical multiplexing, and loop systems. A unified model is developed and then used to analyze the queueing behavior of the star and loop systems. Numerical results for selected traffic intensities and message lengths, given in graphical form, provide insight into the performance of these systems.