Semantic data mining and linked data for a recommender system in the AEC industry

Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations

Knowledge Discovery in Virtual Worlds Usage Data: approaching Web Mining concepts to 3D Virtual Environments

[EN] This paper examines the relationships between Web and Virtual Worlds, and how these relationships can be used to approach concepts of knowledge discovery from Web Mining to 3D environments, such as Virtual Worlds. Also it will explain how to track information of usage data for knowledge discovery and what goals can be planned for this process. Every theoretical concept will be shown with examples, including the usage options to collect, data input to the entire process, relevant information extraction from raw data, techniques to discover knowledge and several considerations to decide and represent what knowledge is useful for the user. Based on these concepts a framework is presented in which, by comparison and approach to Web Usage Mining, may be defined an entire process of Knowledge Discovery and Data Analysis

Building Knowledge Management System for Researching Terrorist Groups on the Web

Nowadays, terrorist organizations have found a cost-effective resource to advance their courses by posting high-impact Web sites on the Internet. This alternate side of the Web is referred to as the “Dark Web.” While counterterrorism researchers seek to obtain and analyze information from the Dark Web, several problems prevent effective and efficient knowledge discovery: the dynamic and hidden character of terrorist Web sites, information overload, and language barrier problems. This study proposes an intelligent knowledge management system to support the discovery and analysis of multilingual terrorist-created Web data. We developed a systematic approach to identify, collect and store up-to-date multilingual terrorist Web data. We also propose to build an intelligent Web-based knowledge portal integrated with advanced text and Web mining techniques such as summarization, categorization and cross-lingual retrieval to facilitate the knowledge discovery from Dark Web resources. We believe our knowledge portal provide counterterrorism research communities with valuable datasets and tools in knowledge discovery and sharing

Neuro-Fuzzy Based Hybrid Model for Web Usage Mining

AbstractWeb Usage mining consists of three main steps: Pre-processing, Knowledge Discovery and Pattern Analysis. The information gained from the analysis can then be used by the website administrators for efficient administration and personalization of their websites and thus the specific needs of specific communities of users can be fulfilled and profit can be increased. Also, Web Usage Mining uncovers the hidden patterns underlying the Web Log Data. These patterns represent user browsing behaviours which can be employed in detecting deviations in user browsing behaviour in web based banking and other applications where data privacy and security is of utmost importance. Proposed work pre-process, discovers and analyses the Web Log Data of Dr. T.M.A.PAI polytechnic website. A neuro-fuzzy based hybrid model is employed for Knowledge Discovery from web logs

Web Mining Evolution & Comparative Study with Data Mining

Web Technology is evolving very fast and Internet Users are growing much faster than estimated. The website users are using a wide range of websites leaving back a variety of information. This information must be used by the websites administrator to manipulate their websites according to the users of the websites. Aim of research in web mining is to develop a new technique for extracting and mining useful information or knowledge from web pages. Thus it?s a challenging task for automated discovery of targeted or unexpected knowledge due to heterogeneity and lack of structure of web data. In this paper we will discuss about the evolution of web mining. This paper will contain detailed description about the other parts of web mining. Paper also analyse data mining and made a comparison between data mining and web mining on the basis of various parameters

Integrating E-Commerce and Data Mining: Architecture and Challenges

We show that the e-commerce domain can provide all the right ingredients for successful data mining and claim that it is a killer domain for data mining. We describe an integrated architecture, based on our expe-rience at Blue Martini Software, for supporting this integration. The architecture can dramatically reduce the pre-processing, cleaning, and data understanding effort often documented to take 80% of the time in knowledge discovery projects. We emphasize the need for data collection at the application server layer (not the web server) in order to support logging of data and metadata that is essential to the discovery process. We describe the data transformation bridges required from the transaction processing systems and customer event streams (e.g., clickstreams) to the data warehouse. We detail the mining workbench, which needs to provide multiple views of the data through reporting, data mining algorithms, visualization, and OLAP. We con-clude with a set of challenges.Comment: KDD workshop: WebKDD 200

Improved Pre-Processing Stages in Web Usage Mining Using Web Log

Enormous growth in the web persists both in number of web sites and number of users. The growth generated large volume of data in during user’s interaction with the web site and recorded in web logs. Web site owners need to understand about their users by accessing these web logs. Web mining perks up to comprehend range of concepts of diverse fields. Web Usage Mining (WUM) is the recent research field that it corresponds to the process of Knowledge Discovery in Databases (KDD). It comprises three main categories: Pre-Processing, Pattern Analysis, Pattern Discovery. WUM extracts behavioral data from web users data and if possible from web site information (structure and content). In this paper, we propose a customized application specific methodology for preprocessing the Web logs and combining WUM with Association Rule Mining

GCG: Mining Maximal Complete Graph Patterns from Large Spatial Data

Recent research on pattern discovery has progressed from mining frequent patterns and sequences to mining structured patterns, such as trees and graphs. Graphs as general data structure can model complex relations among data with wide applications in web exploration and social networks. However, the process of mining large graph patterns is a challenge due to the existence of large number of subgraphs. In this paper, we aim to mine only frequent complete graph patterns. A graph g in a database is complete if every pair of distinct vertices is connected by a unique edge. Grid Complete Graph (GCG) is a mining algorithm developed to explore interesting pruning techniques to extract maximal complete graphs from large spatial dataset existing in Sloan Digital Sky Survey (SDSS) data. Using a divide and conquer strategy, GCG shows high efficiency especially in the presence of large number of patterns. In this paper, we describe GCG that can mine not only simple co-location spatial patterns but also complex ones. To the best of our knowledge, this is the first algorithm used to exploit the extraction of maximal complete graphs in the process of mining complex co-location patterns in large spatial dataset.Comment: 1