An algebraic transformation framework for multidatabase queries
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: Myriad: Design and Implementation of a Federated Database Prototype
abstract: A key problem in providing ‘enterprise-wide’ information is the integration of databases that have been independently developed. An important requirement is to accommodate heterogeneity and maintain the autonomy of component databases. Myriad is a federated database prototype developed at the University of Minnesota, to provide a testbed for investigating alternatives in architecture and algorithms for database integration, query processing and optimization, and concurrency control and recovery. The system incorporates our group’s research results in these areas. This paper describes our experiences in the design and implementation of Myriad, and in the project management. Special emphasis is given to discussing design alternatives and their impact on Myriad. This paper also presents the software engineering principles and the project management techniques we used in developing Myriad and the lessons we learned. We believe these lessons would be useful for practitioners who wish to develop a similar system. Handling heterogeneity and autonomy were prime objectives throughout the prototyping effort. We are convinced that a prototype federated database is an important infrastructural requirement for the overall goal of ‘enterprise-integration’, and believe Myriad to be a significant contribution towards this.

title: A Case for Analytical Customer Relationship Management
abstract: The Internet has emerged as a low cost, low latency and high bandwidth customer communication channel. Its interactive nature provides an organization the ability to enter into a close, personalized dialog with individual customers. The simultaneous maturation of data management technologies like data warehousing, and data mining, have created the ideal environment for making customer relationship management (CRM) a much more systematic effort than it has been in the past. In this paper we described how data analytics can be used to make various CRM functions like customer segmentation, communication targeting, retention, and loyalty much more effective. We briefly describe the key technologies needed to implement analytical CRM, and the organizational issues that must be carefully handled to make CRM a reality. Our goal is to illustrate problems that exist with current CRM efforts, and how using data analytics techniques can address them. Our hope is to get the data mining community interested in this important application domain.

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: VDBMS: A Solution to Integrating Heterogeneous Data Sources

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.