Automatic categorization of query results
Exploratory ad-hoc queries could return too many answers - a phenomenon commonly referred to as "information overload". In this paper, we propose to automatically categorize the results of SQL queries to address this problem. We dynamically generate a labeled, hierarchical category structure - users can determine whether a category is relevant or not by examining simply its label; she can then explore just the relevant categories and ignore the remaining ones, thereby reducing information overload. We first develop analytical models to estimate information overload faced by a user for a given exploration. Based on those models, we formulate the categorization problem as a cost optimization problem and develop heuristic algorithms to compute the min-cost categorization.

title: Enabling soft queries for data retrieval
abstract: Data retrieval finding relevant data from large databases - has become a serious problem as myriad databases have been brought online in the Web. For instance, querying the for-sale houses in Chicago from realtor.com returns thousands of matching houses. Similarly, querying ''digital camera'' in froogle.com returns hundreds of thousand of results. This data retrieval is essentially an online ranking problem, i.e., ranking data results according to the user's preference effectively and efficiently. This paper proposes a new rank query framework, for effectively incorporating ''user-friendly'' rank-query formulation into ''data base (DB)-friendly'' rank-query processing, in order to enable ''soft'' queries on databases. Our framework assumes, as the ''back-end,'' the score-based ranking model for expressive and efficient query processing. On top of the score-based model, as the ''front-end,'' we adopt an SVM-ranking mechanism for providing intuitive and exploratory query formulation. In essence, our framework enables users to formulate queries simply by ordering some sample objects, while learning the ''DB-friendly'' ranking function F from the partial orders. Such learned functions can then be processed and optimized by existing database systems. We demonstrate the efficiency and effectiveness of our framework using real-life user queries and datasets: our results show that the system effectively learns quantitative ranking functions from qualitative feedback from users with efficient online processing.

title: Clustering High Dimensional Massive Scientific Datasets
abstract: Many scientific applications can benefit from an efficient clustering algorithm of massively large high dimensional datasets. However most of the developed algorithms are impractical to use when the amount of data is very large. Given N objects each defined by an M-dimensional feature vector, any clustering technique for handling very large datasets in high dimensional space should run in time O(MN) at best, and O(MN log N) in the worst case, using no more than O(MN) storage, for it to be practical. We introduce a hybrid algorithm, called HyCeltyc, for clustering massively large high dimensional datasets in O(MN) time which is linear in the size of the data. HyCeltyc, which stands for HybridCell Density Clustering method, combines a cell-density based algorithm with a hierarchical agglomerative method to identify clusters in linear time. The main steps of the algorithm involve sampling, dimensionality reduction, selection of significant features on which to cluster the data and a grid-based clustering algorihm that is linear in the data size.

title: Disseminating Query Results in Wireless Systems
abstract: . This document describes the planned activities in WINNER II to disseminate and exploit the knowledge gained during the current project runtime. This description is not seen as final and comprehensive, in contrary, a continuous process of opportunity analysis will assure the best possible result exploitation. Keyword list: dissemination, know-how

title: An algebraic transformation framework for multidatabase queries
abstract: Existence of semantic conflicts between component databases severely impacts query processing in a multidatabase system. In this paper, we describe two types of semantic conflicts that have to be dealt with in the integration of databases modeling information about related sets of real-world entities. These are the entityidentification problem and theattribute value conflict problem. While thetwo-way outerjoin operation has been commonly used for resolving entity identification problem between two component relations, outerjoins using regular equality comparisons between component relation keys is shown to produce counter-intuitive entity identification result. We remedy this by defining a newkey-equality comparator in place of regular equality comparator, for outerjoins. For the attribute value conflict problem, we define aGeneralized Attribute Derivation (GAD) operation which allows user-defined attribute derivation functions to be used to compute new attributes from the component relations' attributes. By adding two-way outerjoin andGAD to the set of relational operations, the traditional algebraic transformation framework for relational queries is no longer adequate for multidatabase query processing and optimization. As a result, we introduceconstrained query tree as the multidatabase query representation. We show that some knowledge about query predicates and attribute derivation functions can be used to simplify queries. Such knowledge is modeled as an outerjoin graph attached to every outerjoin operation in the query tree. Based on this, we further extend the traditional algebraic transformation framework to include two-way outerjoins andGAD operations. Our framework demonstrates that properties of selection/join predicates and attribute derivation functions can be used to provide interesting transformation alternatives. This framework also serves as a formal ground for developing optimization strategies for multidatabase queries.

title: Personal Workflow Management in Support of Pervasive Computing
abstract: With the advent of new wireless technologies, mobile personal computers (MPCs) are quickly gaining their popularity. Concurrently an increasingly number of services for MPCs have been developed. However, current services view PMCs as a vehicle to perform individual activities. No attempt has been made to provide process services that reach mobile users via their PMCs. In the real world, many personal applications are process-oriented, e.g., planning a trip, holding a party, applying a new job, etc. We expect PMCs to help manage and execute these processes in the future, serving the role of planning, coordinating, and reminding task executions so as to accomplish predefined personal goals. On the other hand, traditional workflow management systems are mainly used to coordinate business processes in enterprises. These processes must be repetitive and have well-formed structures. Personal processes differ from their business counterparts in that they are subject to change and rigid regulations on control flow are seldom imposed. As a result, personal activities are related mainly due to their data dependencies and their coordination must be flexible. These differences demand a new personal workflow model as well as the innovative design of a personal workflow system (PWFS).

title: VDBMS: A Solution to Integrating Heterogeneous Data Sources

title: Efficient Group Pattern Mining Using Data Summarization
abstract: In group pattern mining, we discover group patterns from a given user movement database based on their spatio-temporal distances. When both the number of users and the logging duration are large, group pattern mining algorithms become very inefficient. In this paper, we therefore propose a spherical location summarization method to reduce the overhead of mining valid 2-groups. In our experiments, we show that our group mining algorithm using summarized data may require much less execution time than that using non-summarized data.