Su Myeon Kim

Bio: Su Myeon Kim is an academic researcher from KAIST. The author has contributed to research in topics: Service (business) & Compensating transaction. The author has an hindex of 5, co-authored 9 publications receiving 113 citations. Previous affiliations of Su Myeon Kim include Samsung.

Maintaining consistency under isolation relaxation of web services transactions

Abstract: For efficiently managing Web Services (WS) transactions which are executed across multiple loosely-coupled autonomous organizations, isolation is commonly relaxed. A Web services operation of a transaction releases locks on its resources once its jobs are completed without waiting for the completions of other operations. However, those early unlocked resources can be seen by other transactions, which can spoil data integrity and causes incorrect outcomes. Existing WS transaction standards do not consider this problem. In this paper, we propose a mechanism to ensure the consistent executions of isolation-relaxing WS transactions. The mechanism effectively detects inconsistent states of transactions with a notion of a completion dependency and recovers them to consistent states. We also propose a new Web services Transaction Dependency management Protocol (WTDP). WTDP helps organizations manage the WS transactions easily without data inconsistency. WTDP is designed to be compliant with a representative WS transaction standard, the Web Services Transactions specifications, for easy integration into existing WS transaction systems. We prototyped a WTDP-based WS transaction management system to validate our protocol.

A framework for ensuring consistency of Web Services Transactions

Abstract: For efficiently managing Web Services (WS) transactions which are executed across multiple loosely-coupled autonomous organizations, isolation is commonly relaxed. A Web service operation of a transaction releases locks on its resources once its jobs are completed without waiting for the completions of other operations. However, those early unlocked resources can be seen by other transactions, which can spoil data integrity and cause incorrect outcomes. Existing WS transaction standards do not consider this problem. In this paper, we propose a mechanism to ensure the consistent executions of isolation-relaxing WS transactions. The mechanism effectively detects inconsistent states of transactions with a notion of an end-state dependency and recovers them to consistent states. We also propose a new Web services Transaction Dependency management Protocol (WTDP). WTDP helps organizations manage the WS transactions easily without data inconsistency. WTDP is designed to be compliant with a representative WS transaction standard, the Web Services Transactions specifications, for easy integration into existing WS transaction systems. We prototyped a WTDP-based WS transaction management system to validate our protocol.

DCF: an efficient data stream clustering framework for streaming applications

Abstract: Streaming applications, such as environment monitoring and vehicle location tracking require handling high volumes of continuously arriving data and sudden fluctuations in these volumes while efficiently supporting multi-dimensional historical queries. The use of the traditional database management systems is inappropriate because they require excessive number of disk I/O in continuously updating massive data streams. In this paper, we propose DCF (Data Stream Clustering Framework), a novel framework that supports efficient data stream archiving for streaming applications. DCF can reduce a great amount of disk I/O in the storage system by grouping incoming data into clusters and storing them instead of raw data elements. In addition, even when there is a temporary fluctuation in the amount of incoming data, it can stably support storing all incoming raw data by controlling the cluster size. Our experimental results show that our approach significantly reduces the number of disk accesses in terms of both inserting and retrieving data.

CIGMA: active inventory service in global e-market for enabling one-stop shopping over Internet shopping sites

Abstract: A fully connected Internet business environment will introduce a high level of dynamics to the business process. It may result in very frequent changes in business decisions and thus, information for various items may undergo constant change. In addition, there could be a flood of similar shopping sites. In such a highly dynamic environment, ordinary online customers may feel that online shopping is not comfortable. Existing service models or systems cannot effectively reflect such a dynamic environment and support ordinary online customers. We propose a new e-commerce service called CIGMA. CIGMA provides catalog comparison and purchase mediation services over multiple shopping sites for ordinary online customers. The service is based on up-to-date information by reflecting frequent changes in catalog information in real-time. It also matches the desire of online customers for fast response since the service is provided based on data cached in a high performance caching system. An important and challenging issue in realizing the CIGMA service is design of the merchant-side interface. This paper presents CIGMA, focusing on its merchant-side interface including service setup and deployment procedure.

Data stream clustering: A survey

Abstract: Data stream mining is an active research area that has recently emerged to discover knowledge from large amounts of continuously generated data. In this context, several data stream clustering algorithms have been proposed to perform unsupervised learning. Nevertheless, data stream clustering imposes several challenges to be addressed, such as dealing with nonstationary, unbounded data that arrive in an online fashion. The intrinsic nature of stream data requires the development of algorithms capable of performing fast and incremental processing of data objects, suitably addressing time and memory limitations. In this article, we present a survey of data stream clustering algorithms, providing a thorough discussion of the main design components of state-of-the-art algorithms. In addition, this work addresses the temporal aspects involved in data stream clustering, and presents an overview of the usually employed experimental methodologies. A number of references are provided that describe applications of data stream clustering in different domains, such as network intrusion detection, sensor networks, and stock market analysis. Information regarding software packages and data repositories are also available for helping researchers and practitioners. Finally, some important issues and open questions that can be subject of future research are discussed.

BATON: a balanced tree structure for peer-to-peer networks

Abstract: We propose a balanced tree structure overlay on a peer-to-peer network capable of supporting both exact queries and range queries efficiently. In spite of the tree structure causing distinctions to be made between nodes at different levels in the tree, we show that the load at each node is approximately equal. In spite of the tree structure providing precisely one path between any pair of nodes, we show that sideways routing tables maintained at each node provide sufficient fault tolerance to permit efficient repair. Specifically, in a network with N nodes, we guarantee that both exact queries and range queries can be answered in O(log N) steps and also that update operations (to both data and network) have an amortized cost of O(log N). An experimental assessment validates the practicality of our proposal.

Challenges for Database Management in the Internet of Things

Abstract: This article discusses the challenges for Database Management in the Internet of Things. We provide scenarios to illustrate the new world that will be produced by the Internet of Things, where physical objects are fully integrated into the information highway. We discuss the different types of data that will be part of the Internet of Things. These include identification, positional, environmental, historical, and descriptive data. We consider the challenges brought by the need to manage vast quantities of data across heterogeneous systems. In particular, we consider the areas of querying, indexing, process modeling, transaction handling, and integration of heterogeneous systems. We refer to the earlier work that might provide solutions for these challenges. Finally we discuss a road map for the Internet of Things and respective technical priorities.

Online fuzzy c means

Abstract: Clustering streaming data presents the problem of not having all the data available at one time. Further, the total size of the data may be larger than will fit in the available memory of a typical computer. If the data is very large, it is a challenge to apply fuzzy clustering algorithms to get a partition in a timely manner. In this paper, we present an online fuzzy clustering algorithm which can be used to cluster streaming data, as well as very large data sets which might be treated as streaming data. Results on several large volumes of magnetic resonance images show that the new algorithm produces partitions which are very close to what you could get if you clustered all the data at one time. So, the algorithm is an accurate approach for online clustering.