In-Chul Song

Rule-based Dynamic Business Process Modification and Adaptation

The advances in ubiquitous computing technologies have given rise to the u-Business, where we obtain business-related context information from ubiquitous devices to adapt to the changes of business environments in a timely manner. In these rapidly changing u-Business environments, we need a mechanism to modify business process schemas, which represent a group of business activities arranged in a meaningful way to give values to the u-Business. We also need a way to migrate running process instances to their new business process schemas. In this paper, we propose a rule-driven approach for dynamic business process schema modification and instance adaptation. The proposed approach uses rules to describe schema modification operations to be applied and rule execution conditions. Business environment changes are encoded in these rules, thus timely schema modification is possible. We also propose a system architecture for realizing this rule-driven approach. We have designed the system architecture by utilizing existing technologies so that the system can be easily implemented.

Energy-efficient two-dimensional skyline query processing in wireless sensor networks

Wireless sensor networks have been widely used for environment monitoring and event surveillance. In recent years, the skyline query, as an important operator for multiple-criteria decision making, has received much attention because of its many real-world applications. In this paper, we propose a new filter-based method, called SKYFILTER, for skyline query processing. Our method provides an enhanced efficiency by reduction of the total wireless communication between sensor nodes. In addition, the proposed method also provides a better performance than other existing methods with respect to the computational efficiency of skyline computation in sensor nodes. In this paper, we first present a new multiple filter-based technique to reduce wireless communication. Then, we provide a new merge sort-based algorithm for efficient skyline computation in sensor nodes. We show through performance experiments and algorithm analysis that the proposed method generally provides a better performance than other existing methods in terms of the wireless communication cost and computational efficiency.

An edge-based framework for fast subgraph matching in a large graph

In subgraph matching, we want to find all subgraphs of a database graph that are isomorphic to a query graph. Subgraph matching requires subgraph isomorphism testing, which is NP-complete. Recently, some techniques specifically designed for subgraph matching in a large graph have been proposed. They are based on a filtering-and-verification framework. In the filtering phase, they filter out vertices that are not qualified for subgraph isomorphism testing. In the verification phase, subgraph isomorphism testing is performed and all matched subgraphs are returned to the user. We call them a vertex-based framework in the sense that they use vertex information when filtering out unqualified vertices. Edge information, however, can also be used for efficient subgraph matching. In this paper, we propose an edge-based framework for fast subgraph matching in a large graph. By using edge connectivity information, our framework not only filters out more vertices in the filtering phase, but also avoids unnecessary edge connectivity checking operations in the verification phase. The experimental results show that our method significantly outperforms existing approaches for subgraph matching in a large graph.

Content-based multipath routing for sensor networks

In wireless sensor networks, in-network processing of aggregation queries has been an important technique to reduce energy consumption in wireless communication, which is a main source of energy consumption in sensor devices. In-network processing is typically guided by an aggregation tree, where each node forwards partially computed aggregates to its parent. In this paper we consider a routing method for grouped aggregation queries, where sensor readings are divided into disjoint groups according to their values and aggregates are computed for each group. For this type of queries, multipath routing, where each node forwards different aggregates to different nodes, can lead to more efficient in-network processing. However, no multipath routing protocol for efficient in-network processing of grouped aggregation queries has been proposed thus far. In this paper we propose a new routing protocol, called Content-based Multipath Routing (CMR), for efficient in-network processing of grouped aggregation queries. CMR employs multipath routing, and each node forwards partially computed aggregates along different paths based on the contents of the aggregates. The experimental results show that CMR outperforms the existing aggregation tree-based routing protocols.

OntLMS: An Ontology-Based Lifelog Management System

In this paper, we propose an ontology-based lifelog management system, called OntLMS, which is built by utilizing the existing lifelog management system based on the relational data model. We design ontology for modelling lifelog data and provide both one-time and incremental conversion methods that transform lifelog data in the relational database into ontology. Our system supports expressive, user-centric queries such as a structured query and a keyword query, as well as a visual query. This paper shares the lessons learned from our experience on lifelog management, data migration over different data models, and ontology design.