Incorporating data sources and methodologies for crime data mining

2011 ◽  
Author(s):  
Fatih Ozgul ◽  
Claus Atzenbeck ◽  
Ahmet Celik ◽  
Zeki Erdem
Keyword(s):  
Data Mining ◽  
Data Sources ◽  
Crime Data

scholarly journals An Ontology-based Visual Analytics for Apple Variety Testing

2021 ◽  
Author(s):  
Ekaterina Chuprikova ◽  
Abraham Mejia Aguilar ◽  
Roberto Monsorno
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Data Integration ◽  
Visual Analytics ◽  
Agricultural Sector ◽  
Environmental Data ◽  
Data Sources ◽  
Apple Variety ◽  
Testing Program ◽  
Variety Testing

<p>Increasing agricultural production challenges, such as climate change, environmental concerns, energy demands, and growing expectations from consumers triggered the necessity for innovation using data-driven approaches such as visual analytics. Although the visual analytics concept was introduced more than a decade ago, the latest developments in the data mining capacities made it possible to fully exploit the potential of this approach and gain insights into high complexity datasets (multi-source, multi-scale, and different stages). The current study focuses on developing prototypical visual analytics for an apple variety testing program in South Tyrol, Italy. Thus, the work aims (1) to establish a visual analytics interface enabled to integrate and harmonize information about apple variety testing and its interaction with climate by designing a semantic model; and (2) to create a single visual analytics user interface that can turn the data into knowledge for domain experts. </p><p>This study extends the visual analytics approach with a structural way of data organization (ontologies), data mining, and visualization techniques to retrieve knowledge from an extensive collection of apple variety testing program and environmental data. The prototype stands on three main components: ontology, data analysis, and data visualization. Ontologies provide a representation of expert knowledge and create standard concepts for data integration, opening the possibility to share the knowledge using a unified terminology and allowing for inference. Building upon relevant semantic models (e.g., agri-food experiment ontology, plant trait ontology, GeoSPARQL), we propose to extend them based on the apple variety testing and climate data. Data integration and harmonization through developing an ontology-based model provides a framework for integrating relevant concepts and relationships between them, data sources from different repositories, and defining a precise specification for the knowledge retrieval. Besides, as the variety testing is performed on different locations, the geospatial component can enrich the analysis with spatial properties. Furthermore, the visual narratives designed within this study will give a better-integrated view of data entities' relations and the meaningful patterns and clustering based on semantic concepts.</p><p>Therefore, the proposed approach is designed to improve decision-making about variety management through an interactive visual analytics system that can answer "what" and "why" about fruit-growing activities. Thus, the prototype has the potential to go beyond the traditional ways of organizing data by creating an advanced information system enabled to manage heterogeneous data sources and to provide a framework for more collaborative scientific data analysis. This study unites various interdisciplinary aspects and, in particular: Big Data analytics in the agricultural sector and visual methods; thus, the findings will contribute to the EU priority program in digital transformation in the European agricultural sector.</p><p>This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 894215.</p>

Data mining for building knowledge bases: techniques, architectures and applications

2016 ◽  
Vol 31 (2) ◽  
pp. 97-123 ◽  
Author(s):  
Alfred Krzywicki ◽  
Wayne Wobcke ◽  
Michael Bain ◽  
John Calvo Martinez ◽  
Paul Compton
Keyword(s):  
Data Mining ◽  
Knowledge Base ◽  
Question Answering ◽  
Knowledge Bases ◽  
Event Extraction ◽  
Data Sources ◽  
Small Scale ◽  
Knowledge Mining ◽  
Practical Applications ◽  
Unstructured Text

AbstractData mining techniques for extracting knowledge from text have been applied extensively to applications including question answering, document summarisation, event extraction and trend monitoring. However, current methods have mainly been tested on small-scale customised data sets for specific purposes. The availability of large volumes of data and high-velocity data streams (such as social media feeds) motivates the need to automatically extract knowledge from such data sources and to generalise existing approaches to more practical applications. Recently, several architectures have been proposed for what we callknowledge mining: integrating data mining for knowledge extraction from unstructured text (possibly making use of a knowledge base), and at the same time, consistently incorporating this new information into the knowledge base. After describing a number of existing knowledge mining systems, we review the state-of-the-art literature on both current text mining methods (emphasising stream mining) and techniques for the construction and maintenance of knowledge bases. In particular, we focus on mining entities and relations from unstructured text data sources, entity disambiguation, entity linking and question answering. We conclude by highlighting general trends in knowledge mining research and identifying problems that require further research to enable more extensive use of knowledge bases.

Informational Data Mining

2013 ◽  
pp. 58-65
Author(s):  
Feyza Gürbüz ◽  
Fatma Gökçe Önen
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Knowledge Discovery ◽  
Major Change ◽  
Research Community ◽  
Data Sources ◽  
Accurate Information ◽  
Rule Mining ◽  
Data Mining Techniques ◽  
Information Strategies

The previous decades have witnessed major change within the Information Systems (IS) environment with a corresponding emphasis on the importance of specifying timely and accurate information strategies. Currently, there is an increasing interest in data mining and information systems optimization. Therefore, it makes data mining for optimization of information systems a new and growing research community. This chapter surveys the application of data mining to optimization of information systems. These systems have different data sources and accordingly different objectives for knowledge discovery. After the preprocessing stage, data mining techniques can be applied on the suitable data for the objective of the information systems. These techniques are prediction, classification, association rule mining, statistics and visualization, clustering and outlier detection.

Proposal of Analytical Model for Business Problems Solving in Big Data Environment

Web Services ◽  
2019 ◽  
pp. 618-638
Author(s):  
Goran Klepac ◽  
Kristi L. Berg
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Predictive Models ◽  
Analytical Approach ◽  
Fraud Detection ◽  
Analytical Techniques ◽  
Data Sources ◽  
Business Decisions ◽  
Mining Projects ◽  
Structured Approach

This chapter proposes a new analytical approach that consolidates the traditional analytical approach for solving problems such as churn detection, fraud detection, building predictive models, segmentation modeling with data sources, and analytical techniques from the big data area. Presented are solutions offering a structured approach for the integration of different concepts into one, which helps analysts as well as managers to use potentials from different areas in a systematic way. By using this concept, companies have the opportunity to introduce big data potential in everyday data mining projects. As is visible from the chapter, neglecting big data potentials results often with incomplete analytical results, which imply incomplete information for business decisions and can imply bad business decisions. The chapter also provides suggestions on how to recognize useful data sources from the big data area and how to analyze them along with traditional data sources for achieving more qualitative information for business decisions.

Critical and Future Trends in Data Mining

Data Mining ◽  
2011 ◽  
pp. 437-452 ◽  
Author(s):  
Jeffrey Hsu
Keyword(s):  
Data Mining ◽  
Business Education ◽  
Scientific Community ◽  
World Wide ◽  
Data Sources ◽  
Knowledge Discovery In Databases ◽  
Future Trends ◽  
Online Data ◽  
Usable Knowledge ◽  
The World

Every day, enormous amounts of information are generated from all sectors, whether it be business, education, the scientific community, the World Wide Web (WWW), or one of many readily available off-line and online data sources. From all of this, which represents a sizable repository of data and information, it is possible to generate worthwhile and usable knowledge. As a result, the field of Data Mining (DM) and knowledge discovery in databases (KDD) has grown in leaps and bounds and has shown great potential for the future (Han & Kamber, 2001). The purpose of this chapter is to survey many of the critical and future trends in the field of DM, with a focus on those which are thought to have the most promise and applicability to future DM applications.

Gaining Strategic Advantage Through Bibliomining

2008 ◽  
pp. 2673-2687
Author(s):  
Scott Nicholson ◽  
Jeffrey Stanton
Keyword(s):  
Data Mining ◽  
Customer Service ◽  
Information Services ◽  
Strategic Decision ◽  
Data Sources ◽  
Strategic Decision Making ◽  
Staff Members ◽  
Library Users ◽  
Search Patterns ◽  
Patterns Of Behavior

Library and information services in corporations, schools, universities and communities capture information about their users, circulation history, resources in the collection and search patterns (Koenig, 1985). Unfortunately, few libraries have taken advantage of these data as a way to improve customer service, manage acquisition budgets or influence strategic decision making about uses of information in their organizations. In this chapter, we present a global view of the data generated in libraries, and the variety of decisions that those data can inform. We describe ways in which library and information managers can use data mining in their libraries, i.e., bibliomining, to understand patterns of behavior among library users and staff members and patterns of information resource use throughout the institution. The chapter examines data sources and possible applications of data mining techniques in the library.

Data Mining and Homeland Security

2011 ◽  
pp. 1323-1331
Author(s):  
Jeffrey W. Seifert
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Homeland Security ◽  
Predictive Analytics ◽  
New Technology ◽  
Analytical Techniques ◽  
Data Sources ◽  
High Expectations ◽  
Multiple Data ◽  
Factual Data

A significant amount of attention appears to be focusing on how to better collect, analyze, and disseminate information. In doing so, technology is commonly and increasingly looked upon as both a tool, and, in some cases, a substitute, for human resources. One such technology that is playing a prominent role in homeland security initiatives is data mining. Similar to the concept of homeland security, while data mining is widely mentioned in a growing number of bills, laws, reports, and other policy documents, an agreed upon definition or conceptualization of data mining appears to be generally lacking within the policy community (Relyea, 2002). While data mining initiatives are usually purported to provide insightful, carefully constructed analysis, at various times data mining itself is alternatively described as a technology, a process, and/or a productivity tool. In other words, data mining, or factual data analysis, or predictive analytics, as it also is sometimes referred to, means different things to different people. Regardless of which definition one prefers, a common theme is the ability to collect and combine, virtually if not physically, multiple data sources, for the purposes of analyzing the actions of individuals. In other words, there is an implicit belief in the power of information, suggesting a continuing trend in the growth of “dataveillance,” or the monitoring and collection of the data trails left by a person’s activities (Clarke, 1988). More importantly, it is clear that there are high expectations for data mining, or factual data analysis, being an effective tool. Data mining is not a new technology but its use is growing significantly in both the private and public sectors. Industries such as banking, insurance, medicine, and retailing commonly use data mining to reduce costs, enhance research, and increase sales. In the public sector, data mining applications initially were used as a means to detect fraud and waste, but have grown to also be used for purposes such as measuring and improving program performance. While not completely without controversy, these types of data mining applications have gained greater acceptance. However, some national defense/homeland security data mining applications represent a significant expansion in the quantity and scope of data to be analyzed. Moreover, due to their security-related nature, the details of these initiatives (e.g., data sources, analytical techniques, access and retention practices, etc.) are usually less transparent.

Depressive Person Detection using Social Asian Elephants' (SAE) Algorithm over Twitter Posts

2019 ◽  
Vol 9 (4) ◽  
pp. 37-51
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Decision Tree ◽  
Social Life ◽  
Naive Bayes ◽  
Data Sources ◽  
Asian Elephants ◽  
Person Detection ◽  
The Social ◽  
The Web

With the advent of the web and the explosion of data sources such as opinion sites, blogs and microblogs appeared the need to analyze millions of posts, tweets or opinions in order to find out what thinks the net surfers. The idea was to produce a new algorithm inspired by the social life of Asian elephants to detect a person in depressive situation through the analysis of twitter social network. The proposal algorithm gives better performance compared to data mining and bioinspired techniques such as naive Bayes, decision tree, heart lungs algorithm, social cockroach's algorithm.

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