Arbee L. P. Chen

Mermaid—A front-end to distributed heterogeneous databases

Mermaid is a system that allows the user of multiple databases stored under various relational DBMSs running on different machines to manipulate the data using a common language, either ARIEL or SQL. It makes the complexity of this distributed, heterogeneous data processing transparent to the user. In this paper, we describe the architecture, system control, user interface, language and schema translation, query optimization, and network operation of the Mermaid system. Future research issues are also addressed.

A music recommendation system based on music data grouping and user interests

With the growth of the World Wide Web, a large amount of music data is available on the Internet. In addition to searching expected music objects for users, it becomes necessary to develop a recommendation service. In this paper, we design the Music Recommendation System (MRS) to provide a personalized service of music recommendation. The music objects of MIDI format are first analyzed. For each polyphonic music object, the representative track is first determined, and then six features are extracted from this track. According to the features, the music objects are properly grouped. For users, the access histories are analyzed to derive user interests. The content-based, collaborative and statistics-based recommendation methods are proposed, which are based on the favorite degrees of the users to the music groups. A series of experiments are carried out to show that our approach is feasible.

Discovering nontrivial repeating patterns in music data

A repeating pattern in music data is defined as a sequence of notes which appears more than once in a music object. The themes are a typical kind of repeating patterns. The themes and other nontrivial repeating patterns are important music features which can be used for both content-based retrieval of music data and music data analysis. In this paper, we propose two approaches for fast discovering nontrivial repeating patterns in music objects. In the first approach, we develop a data structure called correlative matrix and its associated algorithms for extracting the repeating patterns. In the second approach, we introduce a string-join operation and a data structure called RP-tree for the same purpose. Experiments are performed to compare these two approaches with others. The results are further analyzed to show the efficiency and the effectiveness of our approaches.

Hiding Sensitive Association Rules with Limited Side Effects

Data mining techniques have been widely used in various applications. However, the misuse of these techniques may lead to the disclosure of sensitive information. Researchers have recently made efforts at hiding sensitive association rules. Nevertheless, undesired side effects, e.g., nonsensitive rules falsely hidden and spurious rules falsely generated, may be produced in the rule hiding process. In this paper, we present a novel approach that strategically modifies a few transactions in the transaction database to decrease the supports or confidences of sensitive rules without producing the side effects. Since the correlation among rules can make it impossible to achieve this goal, in this paper, we propose heuristic methods for increasing the number of hidden sensitive rules and reducing the number of modified entries. The experimental results show the effectiveness of our approach, i.e., undesired side effects are avoided in the rule hiding process. The results also report that in most cases, all the sensitive rules are hidden without spurious rules falsely generated. Moreover, the good scalability of our approach in terms of database size and the influence of the correlation among rules on rule hiding are observed

Optimal index and data allocation in multiple broadcast channels

The issue of data broadcast has received much attention in mobile computing. A periodic broadcast of frequently requested data can reduce the workload of the up-link channel and facilitate data access for the mobile user. Since the mobile units usually have limited battery capacity, the minimization of the access latency for the broadcast data is an important problem. The indexing and scheduling techniques on the broadcast data should be considered. We propose a solution to find the optimal index and data allocation, which minimizes the access latency for any number of broadcast channels. We represent all the possible allocations as a tree in which the optimal one is searched, and propose a pruning strategy based on some properties to greatly reduce the search space. Experiments are performed to show the effectiveness of the pruning strategy. Moreover, we propose two heuristics to solve the same problem when the size of the broadcast data is large.

Efficient repeating pattern finding in music databases

In this paper, we propose an approach for the extraction of the repeating patterns in music objects. A repeating pattern is a sequence of notes which appears more than once in a music object. It is one of the most important music features which can be used for both content-based retrieval of music data and music data analysis. We propose a data structure called correlative matrix and its associated algorithms for extracting all repeating patterns in a music object. Experiments are also performed and the results are analyzed to show the efficiency and the effectiveness of our approach.

A graph-based approach for discovering various types of association rules

Mining association rules is an important task for knowledge discovery. We can analyze past transaction data to discover customer behaviors such that the quality of business decisions can be improved. Various types of association rules may exist in a large database of customer transactions. The strategy of mining association rules focuses on discovering large item sets, which are groups of items which appear together in a sufficient number of transactions. We propose a graph-based approach to generate various types of association rules from a large database of customer transactions. This approach scans the database once to construct an association graph and then traverses the graph to generate all large item sets. Empirical evaluations show that our algorithms outperform other algorithms which need to make multiple passes over the database.

Query by rhythm: an approach for song retrieval in music databases

We propose techniques for retrieving songs by rhythm from music databases. The rhythm of songs is modeled by rhythm strings. The song retrieval problem is then transformed to the string matching problem. In order to allow approximate string matching, we define similarity measures on rhythm strings. An index structure, called L-tree, is proposed to support efficient sub-string matching. Retrieval algorithms based on L-tree are then designed to provide approximate and sub- song retrieval. Experimental results show that this approach is effective and efficient.

Answering heterogeneous database queries with degrees of uncertainty

In heterogeneous database systems,partial values have been used to resolve some schema integration problems. Performing operations on partial values may producemaybe tuples in the query result which cannot be compared. Thus, users have no way to distinguish which maybe tuple is the most possible answer. In this paper, the concept of partial values is generalized toprobabilistic partial values. We propose an approach to resolve the schema integration problems using probabilistic partial values and develop a full set of extended relational operators for manipulating relations containing probabilistic partial values. With this approach, the uncertain answer tuples of a query are associated with degrees of uncertainty (represented by probabilities). That provides users a comparison among maybe tuples and a better understanding on the query results. Besides, extended selection and join are generalized to α-selection and α-join, respectively, which can be used to filter out maybe tuples with low probabilities — those which have probabilities smaller than α.

An efficient approach to discovering knowledge from large databases

We study two problems: mining association rules and mining sequential patterns in a large database of customer transactions. The problem of mining association rules focuses on discovering large itemsets where a large itemset is a group of items which appear together in a sufficient number of transactions; while the problem of mining sequential patterns focuses on discovering large sequences where a large sequence is an ordered list of sets of items which appear in a sufficient number of transactions. We present efficient graph based algorithms to solve these problems. The algorithms construct an association graph to indicate the associations between items and then traverse the graph to generate large itemsets and large sequences, respectively. Our algorithms need to scan the database only once. Empirical evaluations show that our algorithms outperform other algorithms which need to make multiple passes over the database.