A multimedia content is usually read-only and composed of multimedia objects with their spatial and temporal specifications. These specifications given by its author can enforce the display of objects to be well organized for its context. When multimedia contents are serviced in network environment by an on-demand basis, the temporal relationship among the objects can be used to improve the performance of the service. This paper models the temporal relationship as a scenario that represents the presentation order of the objects in a scenario and proposes several scheduling methods that make it possible to rearrange the transmission order of objects in a scenario. As a result, system resources such as computing power and network bandwidth can be highly utilized. Since the temporal relationship of a scenario is static, it is possible to reduce the scheduling overhead of a server by pre-scheduling currently servicing scenarios. In addition, several simulation results are presented in order to compare and analyze the characteristics of the proposed methods.

A data stream is a series of massive unbounded tuples continuously generated at a rapid rate. Continuous queries for data streams should be processed continuously, so that a strict time constraint is required. In most previous research studies, in order to guarantee this constraint, the evaluation order of join predicates in a continuous query is optimized using a greedy strategy. However, because a greedy strategy traces only the first promising plan, it often finds a suboptimal plan. To reduce the possibility of producing a suboptimal plan, in this paper, we propose an improved scheme, k-Extended Greedy Algorithm (k-EGA), that simultaneously examines a set of promising plans and reoptimize an execution plan adaptively. The number of promising plans is flexibly controlled by a user-defined range variable. The scheme verifies the performance of the current plan periodically. If the plan is no longer efficient, a newly optimized plan is generated. The performance of the proposed scheme is verified through various experiments to identify its various characteristics.

This paper proposes an efficient query evaluation scheme for a mediator system intended to integrate heterogeneous computing environment in terms of operating systems, database management systems, and other software. Most of mediator systems transform a global query into a set of sub-queries based on their target remote servers. Each sub-query is evaluated by the query modification method to evaluate a global query. However, it is possible to reduce the evaluation cost of a global query when the results of frequently requested sub-queries are materialized in a mediator. In a mediator, its integrating schema can be incrementally modified and the evaluation frequency of a global query can also be continuously varied. In order to select the optimized set of materialized sub-queries with respect to their current evaluation frequencies, the proposed method applies a decay factor for modeling the recent access behavior of each sub-query. In other words, the latest access of a sub-query gets the highest attention in the selection process of materialized sub-queries. As a result, it is possible to adjust the optimized set of materialized sub-queries adaptively according to the recent changes in the evaluation frequencies of sub-queries. Since finding the optimum solution of this problem is NP-hard, it takes too long to be used in practice when the number of sub-queries is large. Consequently, given the size of mediator storage, the rank-based selection algorithm proposed in this paper finds the set of materialized sub-queries which minimizes the total evaluation cost of global queries in linear search complexity.

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is likely to be changed as time goes by. However, most of mining algorithms or frequency approximation algorithms for a data stream are not able to extract the recent change of information in a data stream adaptively. This is because the obsolete information of old transactions which may be no longer useful or possibly invalid at present is regarded as important as that of recent transactions. This paper proposes an information decay method for finding recent frequent itemsets in a data stream. The effect of old transactions on the mining result of a data steam is gradually diminished as time goes by. Furthermore, the decay rate of information can be flexibly adjusted, which enables a user to define the desired life-time of the information of a transaction in a data stream.

Association mining extracts common relationships among a finite number of categorical data objects in a set of transactions. However, if the data objects are not categorical and potentially unlimited, it is impossible to employ the association mining approach. On the other hand, clustering is suitable for modeling a large number of non-categorical data objects as long as there exists a distance measure among them. Although it has been used to classify data objects in a data set into groups of similar objects based on data similarity, it can be used to extract the properties of similar data objects commonly appearing in a set of transactions. In this paper, a new clustering method, CLOCK, is proposed to find common knowledge such as frequent ranges of similar objects in a set of transactions. The common knowledge of data objects in the transactions can be represented by the occurrence frequency of similar data objects in terms of a transaction as well as the common repetitive ratio of similar data objects in each transaction. Furthermore, the proposed method also addresses how to maintain identified common knowledge as a summarized profile. As a result, any data difference between a newly collected transaction and the common knowledge of past transactions can be easily identified.

For detecting the anomalous behavior of a user effectively, most researches have concentrated on statistical techniques. However, since statistical techniques mainly analyze the average behavior of a user's activities, some anomalies can be detected inaccurately. In addition, it is difficult to model intermittent activities performed periodically. In order to model the normal behavior of a user closely, a set of various features can be employed. Given an activity of a user, the values of those features that are related to the activity represent the behavior of the activity. Furthermore, activities performed in a session of a user can be regarded as a semantically atomic transaction. Although it is possible to apply clustering technique to these values to extract the normal behavior of a user, most of conventional clustering algorithms do not consider any transactional boundary in a data set. In this paper, a transaction-based clustering algorithm for modeling the normal behavior of a user is proposed. Based on the activities of the past transactions, a set of clusters for each feature can be found to represent the normal behavior of a user as a concise profile. As a result, any anomalous behavior in an online transaction of the user can be effectively detected based on the profile of the user.