• Journal of Information Technology and Architecture
• /
• v.11 no.1
• /
• pp.63-73
• /
• 2014

As the advance of WWW, unstructured data including texts are taking users' interests more and more. These unstructured data created by WWW users represent users' subjective opinions thus we can get very useful information such as users' personal tastes or perspectives from them if we analyze appropriately. In this paper, we provide various analysis efficiently for unstructured text documents by taking advantage of OLAP (On-Line Analytical Processing) multidimensional cube technology. OLAP cubes have been widely used for the multidimensional analysis for structured data such as simple alphabetic and numberic data but they didn't have used for unstructured data consisting of long texts. In order to provide multidimensional analysis for unstructured text data, however, Text Cube model has been proposed precently. It incorporates term frequency and inverted index as measurements to search and analyze text databases which play key roles in information retrieval. The primary goal of this paper is to apply this text cube model to a real data set from in an Internet site sharing hotel information and to provide multidimensional analysis for users' reviews on hotels written in texts. To achieve this goal, we first build text cubes for the hotel review data. By using the text cubes, we design and implement the system which provides multidimensional keyword search features to search and to analyze review texts on various dimensions. This system will be able to help users to get valuable guest-subjective summary information easily. Furthermore, this paper evaluats the proposed systems through various experiments and it reveals the effectiveness of the system.

• Journal of Information Technology Services
• /
• v.6 no.1
• /
• pp.127-140
• /
• 2007

Due to the development of information technologies and new businesses related to moving objects, the need for the storage and analysis of moving object data is increasing rapidly. Moving object data have a spatiotemporal nature which is different from typical business data. Therefore, different methods of data storage and analysis are required. This paper proposes a multidimensional data model and data visualization to analyze moving object data efficiently and effectively. We expect that decision makers can understand the movement pattern of moving objects more intuitively through the proposed implementation.

• Communications for Statistical Applications and Methods
• /
• v.22 no.3
• /
• pp.265-276
• /
• 2015

In this paper, we propose hierarchical cluster analysis and multidimensional scaling for joint distribution valued data. Information technology is increasing the necessity of statistical methods for large and complex data. Symbolic Data Analysis (SDA) is an attractive framework for the data. In SDA, target objects are typically represented by aggregated data. Most methods on SDA deal with objects represented as intervals and histograms. However, those methods cannot consider information among variables including correlation. In addition, objects represented as a joint distribution can contain information among variables. Therefore, we focus on methods for joint distribution valued data. We expanded the two well-known exploratory methods using the dissimilarities adopted Hall Type relative projection index among joint distribution valued data. We show a simulation study and an actual example of proposed methods.

• Journal of KIISE:Databases
• /
• v.34 no.1
• /
• pp.84-97
• /
• 2007

Selectivity estimation techniques in query optimization have been used in commercial databases and histograms are popularly used for the selectivity estimation. Recently, the techniques for spatio-temporal databases have been restricted to existing temporal and spatial databases. In addition, the selectivity estimation techniques focused on time-series data such as moving objects. It is also impossible to estimate selectivity for range queries with a time interval. Therefore, we construct two histograms, CMH (current multidimensional histogram) and PMH (past multidimensional histogram), to estimate the selectivity of multidimensional sequence data in spatio-temporal databases and propose effective selectivity estimation methods using the histograms. Furthermore, we solve a problem about the range query using our proposed histograms. We evaluated the effectiveness of histograms for range queries with a time interval through various experimental results.

• Journal of KIISE:Databases
• /
• v.29 no.6
• /
• pp.429-440
• /
• 2002

Temporal databases manage time-evolving data. They provide built-in supports for efficient recording and querying of temporal data. The temporal aggregate in temporal databases is an extension of the conventional aggregate to include time concept on the domain and range of aggregation. This paper focuses on multidimensional temporal aggregation. In a multidimensional temporal aggregate, we use one or more general attributes as well as a time attribute on the range of aggregation, thus it is a useful operation for historical data warehouse, Call Data Records(CDR), etc. In this paper, we propose a structure for multidimensional temporal aggregation, called PTA-tree, and an aggregate processing method based on the PTA-tree. Through analyses and performance experiments, we also compare the PTA-tree with the simple extension of SB-tree that was proposed for temporal aggregation.

• The KIPS Transactions:PartD
• /
• v.11D no.4
• /
• pp.823-834
• /
• 2004

In this research, the similarity search algorithms are provided for large video data streams. A video stream that consists of a number of frames can be expressed by a sequence in the multidimensional data space, by representing each frame with a multidimensional vector By analyzing various characteristics of the sequence, it is partitioned into multiple video segments and clusters which are represented by hyper-rectangles. Using the hyper-rectangles of video segments and clusters, similarity functions between two video streams are defined, and two similarity search algorithms are proposed based on the similarity functions algorithms by hyper-rectangles and by representative frames. The former is an algorithm that guarantees the correctness while the latter focuses on the efficiency with a slight sacrifice of the correctness Experiments on different types of video streams and synthetically generated stream data show the strength of our proposed algorithms.

• Journal of Korea Multimedia Society
• /
• v.11 no.5
• /
• pp.567-578
• /
• 2008

For analyzing a huge amount of web pages available in the Internet, we need to extract the encoded information in web pages. In this paper, we propose a method to extract and convert web information from web pages into XML documents for multidimensional analysis. For extracting information from web pages, we propose two languages: one for describing web information extraction rules based on the object-oriented model, and another for describing regular expressions of HTML tag patterns to search for target information. For multidimensional analysis on XML documents, we propose a method for constructing an XML warehouse and various XML cubes from it like the way we do for relational data. Finally, we show the validness of our method through the application to US patent web pages.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.30 no.2 s.150
• /
• pp.358-368
• /
• 2006

Today apparel finns make much effort to build a long-term relationship with their customers. The studies of salesperson-customer relationship emphasized the importance of customer's commitment on the formation of the continuous salesperson-consumer relationship. Therefore, the current study deals with the psychological relationship commitment development and from this prospective examines how consumers maintain the continuous relationship with a particular salesperson. The data were collected from January to February 2005 and analyzed by using SPSS 11.5 and Amos 5.0 with factor analysis, regression, ANOVA, path analysis. The results are as follows: First, the hypothetical model of multidimensional consumer commitment which showed a better fit of data than the rival model is unintentionally conceptualized. Second, the result showed that the affective commitment did the most effective role among the three dimensions of commitment consulted in this study. Especially the data indicated that for the establishment of the affective commitment in the salesperson-customer relationship. it is very important that a customer has deep trust in salesperson's ability, benevolence and honesty.

• Journal of Industrial Technology
• /
• v.15
• /
• pp.41-50
• /
• 1995

This paper presents a technique for estimating distribution of data stored in a database. This technique is very useful for accurate selectivity estimation, which is essential in query optimization and physical database design. To maintain data distribution, we employ the directory of the multilevel grid file, a multidimensional dynamic file organization. The major advantage of our technique is that data distribution information is maintained dynamically in the multilevel grid file. In contrast, other static methods such as the histogram method use static date structures, which requires periodic restructuring. Furthermore, we propose a method for keeping the abstract information of data distribution in main memory. This is advantageous in the situation where the size of main memory is not sufficient. Finally, We also suggest formulas for calculating selectivies of various queries based on our data distribution information.

• Korean Journal of Computational Design and Engineering
• /
• v.10 no.5
• /
• pp.314-327
• /
• 2005

This paper describes the volumetric data modeling and analysis methods that employ volumetric NURBS or VNURBS that represents heterogeneous objects or fields in multidimensional space. For volumetric data modeling, we formulate the construction algorithms involving the scattered data approximation and the curvilinear grid data interpolation. And then the computational algorithms are presented for the geometric and mathematical analysis of the volume data set with the VNURBS model. Finally, we apply the modeling and analysis methods to various field applications including grid generation, flow visualization, implicit surface modeling, and image morphing. Those application examples verify the usefulness and extensibility of our VNUBRS representation in the context of volume modeling and analysis.