Grid-based Data Mining Research Articles

The Spatial Distribution of Sexual Harassment Sentiment in Twitter

In a cultural landscape where discussions about sexual harassment are often considered taboo, this study leverages social media as a platform for the anonymous sharing of experiences related to sexual harassment in Asia. By focusing on Kuala Lumpur, we applied grid-based data mining to analyze the spatial distribution of tweets related to sexual harassment, revealing significant distribution patterns. Moreover, this study utilized two advanced sentiment analysis methods: the Natural Language Toolkit (NLTK) and Azure Machine Learning (AML), to evaluate the polarity of sentiments expressed in these tweets. The findings demonstrate a notable variance in sentiment analysis results between NLTK and AML, with NLTK classifying a majority of tweets as neutral (63.7%), while AML identified a predominant positive sentiment (70%). This discrepancy highlights the complexities of sentiment analysis and the importance of selecting appropriate tools for specific research contexts. In examining the spatial distribution, it becomes apparent that tweets collected through the identified keywords are dispersed within the boundaries of Kuala Lumpur. However, there is a notable concentration in specific areas, particularly evident in the hotspot encompassing Taman Desa and Pantai Dalam. Understanding the sentiment's location enables us to delve deeper into the pronounced cluster of tweets, examining potential correlations with surrounding social and moral issues. This insight empowers us to address the issue through various means, including the implementation of Crime Prevention through Environmental Design and fostering community-based initiatives.

Analysis on Data Mining Model Objected to Internet of Things

Three data mining models suitable to data processing have been proposed according to some technical challenges on the characteristics data for Internet of Things. The first model is the multilayer data mining model, which consists of data collection, data processing, event processing and data mining service. The second model is distributed data mining model that could be used to solve the problem of data stored in different locations. The third model is grid-based data mining model, which could utilize the potentially unlimited amount of data by using grid. At the same time, the paper learns from “decomposition build theory to solve the data mining problem in the internet of things. Finally, the key issues are discussed about model application.

WS-DAI-DM: An Interface Specification for Data Mining in Grid Environments

Providing the appropriate access means for data mining services in Grid Environment is principal for com- bination of Grid and data mining. The transition from cen- tralized data mining process as they are in traditional tools to Grid-compliant and Grid-based data mining services that can coordinate with each other is important to extract useful and potential knowledge/patterns from distributed data resources. But, data mining process has not been integrated with the current Grid architecture, and there are no related specifications or standards available within the Grid com- munity for handling it. The work discusses the interface specification WS-DAI-DM and related issues of providing the consistent web service interfaces to mine meaningful and hidden information/patterns from distributed data re- sources in Grid environments. WS-DAI-DM can facilitate using data mining tools and developing data mining applica- tions. The proposed specification idea is partially imple- mented in the feasibility study and we give an application scenario about how users can access data mining services in Grid environments conveniently via the proposed mecha- nism.

Meta-learning in grid-based data mining systems

The Weka4GML framework has been designed to meet the requirements of distributed data mining. In this paper, we present the Weka4GML architecture based on WSRF technology for developing meta-learning methods to deal with datasets distributed among data grid. This framework extends the Weka toolkit to support distributed execution of data mining methods, like meta-learning. The architecture and the behaviour of the proposed framework are described in this paper. We also detail the different steps needed to execute a meta-learning process on a Globus environment. Finally, the framework has been discussed and compared to related works.

Service-oriented middleware for distributed data mining on the grid

Distribution of data and computation allows for solving larger problems and executing applications that are distributed in nature. The grid is a distributed computing infrastructure that enables coordinated resource sharing within dynamic organizations consisting of individuals, institutions, and resources. The grid extends the distributed and parallel computing paradigms allowing for resource negotiation and dynamical allocation, heterogeneity, open protocols, and services. Grid environments can be used both for compute-intensive tasks and data intensive applications by exploiting their resources, services, and data access mechanisms. Data mining algorithms and knowledge discovery processes are both compute and data intensive, therefore the grid can offer a computing and data management infrastructure for supporting decentralized and parallel data analysis. This paper discusses how grid computing can be used to support distributed data mining. Research activities in grid-based data mining and some challenges in this area are presented along with some promising future directions for developing grid-based distributed data mining.

Metadata for Managing Grid Resources in Data Mining Applications

The Grid is an infrastructure for resource sharing and coordinated use of those resources in dynamic heterogeneous distributed environments. The effective use of a Grid requires the definition of metadata for managing the heterogeneity of involved resources that include computers, data, network facilities, and software tools provided by different organizations. Metadata management becomes a key issue when complex applications, such as data-intensive simulations and data mining applications, are executed on a Grid. This paper discusses metadata models for heterogeneous resource management in Grid-based data mining applications. In particular, it discusses how resources are represented and managed in the Knowledge Grid, a framework for Grid-enabled distributed data mining. The paper illustrates how XML-based metadata is used to describe data mining tools, data sources, mining models, and execution plans, and how metadata is used for the design and execution of distributed knowledge discovery applications on Grids.