VDBMS: A Solution to Integrating Heterogeneous Data Sources

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: 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: 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: 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.

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: 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: 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.