Myriad: Design and Implementation of a Federated Database Prototype
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: Mining Mobile Group Patterns: A Trajectory-Based Approach
abstract: In this paper, we present a group pattern mining approach to derive the grouping information of mobile device users based on a trajectory model. Group patterns of users are determined by distance threshold and minimum time duration. A trajectory model of user movement is adopted to save storage space and to cope with untracked or disconnected location data. To discover group patterns, we propose ATGP algorithm and TVG-growth that are derived from the Apriori and VG-growth algorithms respectively.

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: 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: 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: Mining exception instances to facilitate workflow exception handling
abstract: The importance of exception handling within the context of workflow management has been widely recognized. While some exceptions are expected at design time and thus can be incorporated into the workflow design via some flexible mechanism, others are totally unexpected. Previous work in handling unexpected workflow exceptions focuses on the run-time support to, for example, allow the rollback of some already completed activities, validate the correctness of dynamic workflow change, and deploy a solution to handle exceptions. Authorized persons are responsible for deriving solutions to handle exceptions. We propose a novel approach to facilitating users in proposing solutions for resolving a given exception. Specifically, our approach scans through the previous records in handling exceptions, looking for those that are close to the current exception. The ways in which those exceptions were handled serve as useful information in determining how to handle the current one. Several algorithms are proposed and evaluated through both theoretical analysis and a synthetic data set.

title: Automatic categorization of query results
abstract: 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: Using Formal Ontology for Integrated Spatial Data Mining
abstract: With increasingly available amount of data on a geographic space, spatial data mining has attracted much attention in a geographic information system (GIS). In contrast to the prevalent research efforts of developing new algorithms, there has been a lack of effort to re-use existing algorithms for varying domain and task. Researchers have not been quite attentive to controlling factors that guide the modification of algorithms suited to differing problems. In this study, ontology is examined as a means to customize algorithms for different purposes. We also propose the conceptual framework for a spatial data mining (system) driven by formal ontology. The case study demonstrated that formal ontology enabled algorithms to reflect concepts implicit in domain, and to adapt to users' view, not to mention unburdened efforts to develop new algorithms repetitively.