Using Visualization to Support Data Mining of Large Existing Databases

In this paper. we present ideas how visualization technology can be used to improve the difficult process of querying very large databases. With our VisDB system, we try to provide visual support not only for the query specification process. but also for evaluating query results and. thereafter, refining the query accordingly. The main idea of our system is to represent as many data items as possible by the pixels of the display device. By arranging and coloring the pixels according to the relevance for the query, the user gets a visual impression of the resulting data set and of its relevance for the query. Using an interactive query interface, the user may change the query dynamically and receives immediate feedback by the visual representation of the resulting data set. By using multiple windows for different parts of the query, the user gets visual feedback for each part of the query and, therefore, may easier understand the overall result. To support complex queries, we introduce the notion of approximate joins which allow the user to find data items that only approximately fulfill join conditions. We also present ideas how our technique may be extended to support the interoperation of heterogeneous databases. Finally, we discuss the performance problems that are caused by interfacing to existing database systems and present ideas to solve these problems by using data structures supporting a multidimensional search of the database

Visualization Techniques For Malware Behavior Analysis

Malware spread via Internet is a great security threat, so studying their behavior is important to identify and classify them. Using SSDT hooking we can obtain malware behavior by running it in a controlled environment and capturing interactions with the target operating system regarding file, process, registry, network and mutex activities. This generates a chain of events that can be used to compare them with other known malware. In this paper we present a simple approach to convert malware behavior into activity graphs and show some visualization techniques that can be used to analyze malware behavior, individually or grouped. © 2011 SPIE.8019The Society of Photo-Optical Instrumentation Engineers (SPIE)Tufte, E.R., (2001) The Visual Display of Quantitative Information, , Graphic PressKeim, D., Visual data mining. Tutorial (1997) Proc. 23rd International Conference on Very Large Data BasesCleveland, W.S., (1993) Visualizing Data, , Hobart PressGrégio, A.R.A., Aplicação de técnicas de data mining para a análise de logs de tráfego tcp/ip (2007) Applied Computing at INPE - Brazilian Institute for Space Research, , Masters dissertationInselberg, A., The plane with parallel coordinates (1985) The Visual Computer, 1 (2), pp. 69-91Inselberg, A., (2009) Parallel Coordinates - Visual Multidimensional Geometry and its Applications, , SpringerKohonen, T., (1997) Self-Organizing Maps, , SpringerBeddow, J., Shape coding of multidimensional data on a mircocomputer display (1990) Proc. of the First IEEE Conference on Visualization, pp. 238-246Keim, D.A., Kriegel, H.-P., Using visualization to support data mining of large existing databases (1993) Proc. IEEE Visualization '93 WorkshopShneiderman, B., Tree visualization with tree-maps: A 2-D space-filling approach (1991) ACM Transactions on Graphics, 11, pp. 92-99www.shadowserver.orgwww.cert.brwww.cert.br/docs/whitepapers/spambotsCalais, P.H., Pires, D.E.V., Guedes, D.O., Meira Jr., W., Hoepers, C., Steding-Jessen, K., A campaign-based characterization of spamming strategies (2008) Proc. of Fifth Conference on E-mail and Anti-Spa

Image mining: issues, frameworks and techniques

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. Despite the development of many applications and algorithms in the individual research fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper

Image mining: trends and developments

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining

Data Mining in Health-Care: Issues and a Research Agenda

While data mining has become a much-lauded tool in business and related fields, its role in the healthcare arena is still being explored. Currently, most applications of data mining in healthcare can be categorized into two areas: decision support for clinical practice, and policy planning/decision making. However, it is challenging to find empirical literature in this area since a substantial amount of existing work in data mining for health care is conceptual in nature. In this paper, we review the challenges that limit the progress made in this area and present considerations for the future of data mining in healthcare

Supporting Data mining of large databases by visual feedback queries

In this paper, we describe a query system that provides visual relevance feedback in querying large databases. Our goal is to support the process of data mining by representing as many data items as possible on the display. By arranging and coloring the data items as pixels according to their relevance for the query, the user gets a visual impression of the resulting data set. Using an interactive query interface, the user may change the query dynamically and receives immediate feedback by the visual representation of the resulting data set. Furthermore, by using multiple windows for different parts of a complex query, the user gets visual feedback for each part of the query and, therefore, may easier understand the overall result. Our system allows to represent the largest amount of data that can be visualized on current display technology, provides valuable feedback in querying the database, and allows the user to find results which, otherwise, would remain hidden in the database