Deer Herd Management Using the Internet: A Comparative Study of California Targeted By Data Mining the Internet

An ongoing project to investigate the use of the internet as an information source for decision support identified the decline of the California deer population as a significant issue. Using Google Alerts, an automated keyword search tool, text and numerical data were collected from a daily internet search and categorized by region and topic to allow for identification of information trends. This simple data mining approach determined that California is one of only four states that do not currently report total, finalized deer harvest (kill) data online and that it is the only state that has reduced the amount of information made available over the internet in recent years. Contradictory information identified by the internet data mining prompted the analysis described in this paper indicating that the graphical information presented on the California Fish and Wildlife website significantly understates the severity of the deer population decline over the past 50 years. This paper presents a survey of how states use the internet in their deer management programs and an estimate of the California deer population over the last 100 years. It demonstrates how any organization can use the internet for data collection and discovery

Topic Tracking for Punjabi Language

This paper introduces Topic Tracking for Punjabi language. Text mining is a field that automatically extracts previously unknown and useful information from unstructured textual data. It has strong connections with natural language processing. NLP has produced technologies that teach computers natural language so that they may analyze, understand and even generate text. Topic tracking is one of the technologies that has been developed and can be used in the text mining process. The main purpose of topic tracking is to identify and follow events presented in multiple news sources, including newswires, radio and TV broadcasts. It collects dispersed information together and makes it easy for user to get a general understanding. Not much work has been done in Topic tracking for Indian Languages in general and Punjabi in particular. First we survey various approaches available for Topic Tracking, then represent our approach for Punjabi. The experimental results are shown

Understanding the Cloud Computing Ecosystem: Results from a Quantitative Content Analysis

An increasing number of companies make use of CloudComputing services in order to reduce costs and increaseflexibility of their IT infrastructure. This has enlivened a debateon the benefits and risks of Cloud Computing, among bothpractitioners and researchers. This study applies quantitativecontent analysis to explore the Cloud Computing ecosystem. Theanalyzed data comprises high quality research articles andpractitioner-oriented articles from magazines and web sites. Weapply n-grams and the cluster algorithm k-means to analyze theliterature. The contribution of this paper is twofold: First, itidentifies the key terms and topics that are part of the CloudComputing ecosystem which we aggregated to a comprehensivemodel. Second, this paper discloses the sentiments of key topicsas reflected in articles from both practice and academia

Investigating German Higher Education Institutions\u27 Transfer Activities: New Measurements Based on Web Mining

In recent years, higher education institutions (HEI) have expanded their involvement in diverse transfer activities (TA), extending beyond traditional teaching and research roles. These TA are often heterogeneous and informal, which makes measuring their full scope and effects challenging. In this article, we propose a new and straightforward to implement approach for mastering this task. In a first step, we theoretically derive three different dimensions of transfer, namely the transfer of knowledge, technology and personnel. For each of these categories, we develop an artificial intelligence (AI) optimized keyword list. Finally, we use these lists and apply web mining techniques and natural language processing (NLP) to measure TA from German HEI. To this end, we analyze a total of 299,229 texts from 376 German HEI websites. Our study shows that our proposed approach represents an effective and valuable tool for measuring TA from HEI and provides a foundation for further research

Development and Performance Evaluation of a Real-Time Web Search Engine

As the World Wide Web continues to grow, the tools to retrieve the information must develop in terms of locating web pages, categorizing content, and retrieving quality pages. Web search engines have enhanced the online experience by making pages easier to find. Search engines have made a science of cataloging page content, but the data can age, becoming outdated and irrelevant. By searching pages in real time in a localized area of the web, information that is retrieved is guaranteed to be available at the time of the search. The real-time search engines intriguing premise provides an overwhelming challenge. Since the web is searched in real time, the engine\u27s execution will take longer than traditional search engines. The challenge is to determine what factors can enhance the performance of the real-time search engine. This research takes a look at three components: traversal methodologies for searching the web, utilizing concurrently executing spiders, and implementing a caching resource to reduce the execution time of the real-time search engine. These components represent some basic methodologies to improve performance. By determining which implementations provide the best response, a better and faster real-time search engine can become a useful searching tool for Internet users

Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories

The present work articulates few case empirical studies on decision making in industrial context. Development of variety of Decision Support System (DSS) under uncertainty and vague information is attempted herein. The study emphases on five important decision making domains where effective decision making may surely enhance overall performance of the organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply chain’s g-resilient index and v) risk assessment in e-commerce exercises. Firstly, decision support systems in relation to robot selection are conceptualized through adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey set theory is also found useful in this regard; and is combined with TODIM approach to identify the best robot alternative. In this work, an attempt is also made to tackle subjective (qualitative) and objective (quantitative) evaluation information simultaneously, towards effective decision making. Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a novel decision support framework is proposed to address g-resilient (green and resilient) supplier selection issues. Green capability of suppliers’ ensures the pollution free operation; while, resiliency deals with unexpected system disruptions. A comparative analysis of the results is also carried out by applying well-known decision making approaches like Fuzzy- TOPSIS and Fuzzy-VIKOR. In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance- Based’ model in combination with grey set theory to deal with 3PL provider selection, considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is articulated to demonstrate application potential of the proposed model. The results, obtained thereof, have been compared to that of grey-TOPSIS approach. Another part of this dissertation is to provide an integrated framework in order to assess gresilient (ecosilient) performance of the supply chain of a case automotive company. The overall g-resilient supply chain performance is determined by computing a unique ecosilient (g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient criteria in accordance to their current status of performance. The study is further extended to analyze, and thereby, to mitigate various risk factors (risk sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are recognized and evaluated in a decision making perspective by utilizing the knowledge acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying parameters are assessed in a subjective manner (linguistic human judgment), rather than exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem context (toward e-commerce success). Risks are now categorized into different levels of severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic). Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect the company’s e-commerce performance are recognized through such categorization. The overall risk extent is determined by aggregating individual risks (under ‘critical’ level of severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then used to obtain structural relationship amongst aforementioned five risk sources. An appropriate action requirement plan is also suggested, to control and minimize risks associated with e-commerce exercises

Evolution von Relationen in temporalen partiten Themen-Graphen

In der vorliegenden Arbeit wird ein Modell zur Darstellung von Relationen unter aufgespürten Themen unterschiedlicher Zeitfenster als Themen-Graph entwickelt. Variieren und Verschieben des Betrachtungszeitraums bildet Beziehungen zwischen Themen in unterschiedlicher Komplexität ab unter Einbeziehung der jeweiligen Themenbedeutung. Evolutionslebenszyklen eines Themas wie auch Änderungen thematischer Relationen werden sichtbar. Dabei können gefundene Themen bekannten Ereignissen zugeordnet werden