Personal Workflow Management in Support of Pervasive Computing
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: On the discovery of process models from their instances
abstract: A thorough understanding of the way in which existing business processes currently practice is essential from the perspectives of both process reengineering and workflow management. In this paper, we present a framework and algorithms that derive the underlying process model from past executions. The process model employs a directed graph for representing the control dependencies among activities and associates a Boolean function on each edge to indicate the condition under which the edge is to be enabled. By modeling the execution of an activity as an interval, we have developed an algorithm that derives the directed graph in a faster, more accurate manner. This algorithm is further enhanced with a noise handling mechanism to tolerate noise, which frequently occur in the real world. Experimental results show that the proposed algorithm outperforms the existing ones in terms of efficiency and quality.

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 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: 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: 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: 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.