Seok-Ju Chun

Similarity search for multidimensional data sequences

Time series data, which are a series of one dimensional real numbers, have been studied in various database applications. We extend the traditional similarity search methods on time series data to support a multidimensional data sequence, such as a video stream. We investigate the problem of retrieving similar multidimensional data sequences from a large database. To prune irrelevant sequences in a database, we introduce correct and efficient similarity functions. Both data sequences and query sequences are partitioned into subsequences, and each of them is represented by a Minimum Bounding Rectangle (MBR). The query processing is based upon these MBRs, instead of scanning data elements of entire sequences. Our method is designed: (1) to select candidate sequences in a database, and (2) to find the subsequences of a selected sequence, each of which falls under the given threshold. The latter is of special importance in the case of retrieving subsequences from large and complex sequences such as video. By using it, we do not need to browse the whole of the selected video stream, but just browse the sub-streams to find a scene we want. We have performed an extensive experiment on synthetic, as well as real data sequences (a collection of TV news, dramas, and documentary videos) to evaluate our proposed method. The experiment demonstrates that 73-94 percent of irrelevant sequences are pruned using the proposed method, resulting in 16-28 times faster response time compared with that of the sequential search.

Partial Prefix Sum Method for Large Data Warehouses

A range-sum query computes aggregate information over a data cube in the query range specified by a user. Existing methods based on the prefix-sum approach use an additional cube called the prefix-sum cube (PC), to store the cumulative sums of data, causing a high space overhead. This space overhead not only leads to extra costs for storage devices, but also causes additional propagations of updates and longer access time on physical devices. In this paper, we propose a new cube called Partial Prefix-sum Cube (PPC) that drastically reduces the space of the PC in a large data warehouse. The PPC decreases the update propagation caused by the dependency between values in cells of the PC. We perform an extensive experiment with respect to various dimensions of the data cube and query sizes, and examine the effectiveness and performance of our proposed method. Experimental results show that the PPC drastically reduces the space requirements, while having reasonable query performances.

Effective similarity search methods for large video data streams

In this paper, we investigate the similarity search methods for large video data sets that are the collection of video clips. A video clip, a sequence of video frames describing a particular event, is represented by a sequence in a multidimensional data space. Each video clip is partitioned into video segments considering temporal relationship among frames, and then similar segments of the clip are grouped into video clusters. Based on these video segments and clusters, we define similarity functions and present two similarity search methods: the HR (hyper-rectangle)-search and the RP (representative point)- search. Experiments on synthetic sequences as well as real video clips show the effectiveness of our proposed methods.

Selectivity Estimation for Optimizing Similarity Query in Multimedia Databases

For multimedia databases, a fuzzy query consists of a logical combination of content based similarity queries on features such as the color and the texture which are represented in continuous dimensions. Since features are intrinsically multi-dimensional, the multi-dimensional selectivity estimation is required in order to optimize a fuzzy query. The histogram is popularly used for the selectivity estimation. But the histogram has the shortcoming. It is difficult to estimate the selectivity of a similarity query, since a typical similarity query has the shape of a hyper sphere and the ranges of features are continuous. In this paper, we propose a curve fitting method using DCT to estimate the selectivity of a similarity query with a spherical shape in multimedia databases. Experiments show the effectiveness of the proposed method.

Approximate aggregate queries with guaranteed error bounds

It is very important to provide analysts with guaranteed error bounds for approximate aggregate queries in many current enterprise applications such as the decision support systems. In this paper, we propose a general technique to provide tight error bounds for approximate results to OLAP range-sum queries. We perform an extensive experiment on diverse data sets, and examine the effectiveness of our proposed method with respect to various dimensions of the data cube and query sizes.

A Study of Similarity Measures on Multidimensional Data Sequences Using Semantic Information

One-dimensional time-series data have been studied in various database applications such as data mining and data warehousing. However, in the current complex business environment, multidimensional data sequences (MDS`) become increasingly important in addition to one-dimensional time-series data. For example, a video stream can be modeled as an MDS in the multidimensional space with respect to color and texture attributes. In this paper, we propose the effective similarity measures on which the similar pattern retrieval is based. An MDS is partitioned into segments, each of which is represented by various geometric and semantic features. The similarity measures are defined on the basis of these segments. Using the measures, irrelevant segments are pruned from a database with respect to a given query. Both data sequences and query sequences are partitioned into segments, and the query processing is based upon the comparison of the features between data and query segments, instead of scanning all data elements of entire sequences.

Efficient Processing method of OLAP Range-Sum Queries in a dynamic warehouse environment

In a data warehouse, users typically search for trends, patterns, or unusual data behaviors by issuing queries interactively. The OLAP range-sum query is widely used in finding trends and in discovering relationships among attributes in the data warehouse. In a recent environment of enterprises, data elements in a data cube are frequently changed. The problem is that the cost of updating a prefix sum cube is very high. In this paper, we propose a novel algorithm which reduces the update cost significantly by an index structure called the Δ-tree. Also, we propose a hybrid method to provide either approximate or precise results to reduce the overall cost of queries. It is highly beneficial for various applications that need quick approximate answers rather than time consuming accurate ones, such as decision support systems. An extensive experiment shows that our method performs very efficiently on diverse dimensionalities, compared to other methods.