— We describe a grid-based approach for enterprise-scale data mining that leverages database technology for I/O parallelism, and on-demand compute servers for compute parallelism in the statistical computations. By enterprise-scale, we mean the highly-automated use of data mining in vertical business applications, where the data is stored on one or more relational database systems, and where a ...

Semantic Text Mining and its Application in Biomedical Domain Illhoi Yoo Xiaohua Hu, Ph.D A huge amount of biomedical knowledge and novel discoveries have been produced and collected in text databases or digital libraries, such as MEDLINE, because the most natural form to store information is text. In order to cope with this pressing text information overload, text mining is employed. However, ...

The biomedical literature can be seen as a large integrated, but unstructured data repository. Extracting facts from literature and making them accessible is approached from two directions: manual curation efforts develop ontologies and vocabularies to annotate gene products based on statements in papers. Text mining aims to automatically identify entities and their relationships in text using ...

The increasing popularity of social media encourages more and more users to participate in various online activities and produces data in an unprecedented rate. Social media data is big, linked, noisy, highly unstructured and incomplete, and differs from data in traditional data mining, which cultivates a new research field social media mining. Social theories from social sciences are helpful t...

In order to text mine email data it is important to address the substantial amount of noise usually contained in the data. This noise can skew the results of data mining and so reduce the effectiveness and efficiency of decision support systems that use these techniques. Ideally the noise is removed in pre-processing. The paper presents a case study of a series of steps to progressively clean a...

Mining biological data is an emergent area at the intersection between bioinformatics and data mining (DM). The intelligent agent based model is a popular approach in constructing Distributed Data Mining (DDM) systems to address scalable mining over large scale distributed data. The nature of associations between different amino acids in proteins has also been a subject of great anxiety. There ...

This paper proposes a general system for computationintensive graph mining tasks that find from a big graph all subgraphs that satisfy certain requirements (e.g., graph matching and community detection). Due to the broad range of applications of such tasks, many single-threaded algorithms have been proposed. However, graphs such as online social networks and knowledge graphs often have billions...

[1] Surface mining of coal and subsequent reclamation represent the dominant land use change in the central Appalachian Plateau (CAP) region of the United States. Hydrologic impacts of surface mining have been studied at the plot scale, but effects at broader scales have not been explored adequately. Broad-scale classification of reclaimed sites is difficult because standing vegetation makes th...

Streaming data are potentially infinite sequence of incoming data at very high speed and may evolve over the time. This causes several challenges in mining large scale high speed data streams in real time. Hence, this field has gained a lot of attention of researchers in previous years. This paper discusses various challenges associated with mining such data streams. Several available stream da...

With the continued growth in demand for mineral resources and China's efforts in increasing investment in geological prospecting, fiscal investment in geological exploration becomes a research hotspot. This paper examines the yearly relationship among fiscal investment in geological exploration of the current term, that of the last term and prices of mining rights over the period 1999-2009. Hin...