Deok-Hwan Kim

Multi-dimensional selectivity estimation using compressed histogram information

The database query optimizer requires the estimation of the query selectivity to find the most efficient access plan. For queries referencing multiple attributes from the same relation, we need a multi-dimensional selectivity estimation technique when the attributes are dependent each other because the selectivity is determined by the joint data distribution of the attributes. Additionally, for multimedia databases, there are intrinsic requirements for the multi-dimensional selectivity estimation because feature vectors are stored in multi-dimensional indexing trees. In the 1-dimensional case, a histogram is practically the most preferable. In the multi-dimensional case, however, a histogram is not adequate because of high storage overhead and high error rates.nIn this paper, we propose a novel approach for the multi-dimensional selectivity estimation. Compressed information from a large number of small-sized histogram buckets is maintained using the discrete cosine transform. This enables low error rates and low storage overheads even in high dimensions. In addition, this approach has the advantage of supporting dynamic data updates by eliminating the overhead for periodical reconstructions of the compressed information. Extensive experimental results show advantages of the proposed approach.

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.

QCluster: relevance feedback using adaptive clustering for content-based image retrieval

The learning-enhanced relevance feedback has been one of the most active research areas in content-based image retrieval in recent years. However, few methods using the relevance feedback are currently available to process relatively complex queries on large image databases. In the case of complex image queries, the feature space and the distance function of the users perception are usually different from those of the system. This difference leads to the representation of a query with multiple clusters (i.e., regions) in the feature space. Therefore, it is necessary to handle disjunctive queries in the feature space.In this paper, we propose a new content-based image retrieval method using adaptive classification and cluster-merging to find multiple clusters of a complex image query. When the measures of a retrieval method are invariant under linear transformations, the method can achieve the same retrieval quality regardless of the shapes of clusters of a query. Our method achieves the same high retrieval quality regardless of the shapes of clusters of a query since it uses such measures. Extensive experiments show that the result of our method converges to the users true information need fast, and the retrieval quality of our method is about 22% in recall and 20% in precision better than that of the query expansion approach, and about 34% in recall and about 33% in precision better than that of the query point movement approach, in MARS.

Distributed similarity search algorithm in distributed heterogeneous multimedia databases

The collection fusion problem in multimedia databases is concerned with the merging of results retrieved by content based retrieval from distributed heterogeneous multimedia databases in order to find the most similar objects to a query object. We propose distributed similarity search algorithms, two heuristic algorithms and an algorithm using the linear regression, to solve this problem. To our knowledge, these algorithms are the first research results in the area of distributed content based retrieval for heterogeneous multimedia databases.

Wideband active small antenna design using the dummy antenna for T-DMB system

The frequency band for T-DMB (terrestrial digital multimedia broadcasting) system in Korea is assigned to the VHF channel 7~13 (174 MHz~216 MHz) and channel 8 and channel 12 are used in Seoul. The length of the lambda/4 monopole with an infinite ground is about 38 cm to make a resonance at 200 MHz. Generally, meander line, high dielectric constant material and parasitic elements have been used to reduce the large size of a T-DMB antenna. But, as the effective size of an antenna is reduced, the bandwidth is getting narrower. So, these passive small antennas can not cover the wideband of T-DMB. In this work, the wideband active small antenna is proposed. We integrated the lambda/32 (4.75 cm) short monopole with LNA (low noise amplifier) for the mobile application. To obtain the wideband characteristic, we used the dummy antenna, which is the 2-port equivalent circuit of real antenna.

Heterogeneous image database selection on the web

Image databases on the Web have heterogeneous characteristics since they use different similarity measures and queries are processed depending on their own schemes. In the content-based image retrieval from distributed sites, it is crucial that the metaserver has the capability to find objects, similar to a given query object in terms of the global similarity measure, from different image databases with different local similarity measures. In this paper, we investigate the problem of finding databases, which contain more objects relevant to a given query than other databases, from many image databases dispersed on the Web. This problem is referred to as a database selection problem.We propose a new selection method to determine candidate databases. The selection of databases is based on the hybrid estimator using a few sample objects and compressed histogram information of image databases. Extensive experiments on a large number of image data demonstrate that our proposed method improves the effectiveness of distributed content-based retrieval in a heterogeneous environment.

Collection fusion using Bayesian estimation of a linear regression model in image databases on the Web

The collection fusion problem of image databases is concerned with retrieving relevant images by content based retrieval from image databases distributed on the Web. While there have been many studies about database selection and collection fusion for text databases, little research has been attempted for the case of image databases. Image databases on the Web have heterogeneous characteristics since they use different similiarity measures and queries are processed depending on their own policies. Our previous study [Inf. Process. Lett. 75 (1-2) (2000) 35] provided three algorithms for this problem. In this paper, the metaserver selects image databases supporting similarity measures that are correlated with a global similarity measure, and then submits a query to them. And, we propose a new algorithm for this metaserver, which exploits a probabilistic technique using Bayesian estimation for a linear regression model. It outperforms the previous approach for diverse sizes of result sets for a query, and its improvement in effectiveness becomes especially large with small sizes of result sets. We also provide a virtual optimal algorithm to which our algorithm is compared. With extensive experiments we show the superiority of the Bayesian method over the others.

Power Management Strategy and Performance Evaluation for OpenStack Object Storage

Object-based storage is an efficient storage solution that can handle unstructured data and shows better security and scalability than traditional block-based storage. However, in terms of power management, Object-based storage writes multiple copies in storage cluster, hence many servers consume unnecessary power in idle state. In order to resolve this problem, it is necessary to apply power management strategy by adjusting power mode of servers in idle state according to their workloads. In this paper, we proposed a new dynamic power management (DPM) method to transform power mode of storage servers dynamically according to workload information sent from proxy server. The experimental result shows that the proposed power management technic reduces total power consumption by 12% in the OpenStack based Swift object storage.