Integrating data and text mining processes for digital library applications

Sanderson, Robert; Watry, Paul

doi:10.1145/1255175.1255188

Cited by 8 publications

(11 citation statements)

References 9 publications

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“…Mining and analysis related to these massive data has been the focus of many scientific researchers. [15] The literature from a variety of database, the analysis of dynamic interdisciplinary science paper changed in each historical period (research trends) over time.…”

Section: Research Status On Analysis Of the Research Trend Of The Scimentioning

confidence: 99%

Analysis of Scientific Papers Research Trend Based on Granular Computing

Wang¹,

Yu²,

Li³

2015

IJHIT

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The purpose of this paper is to deal with the data in large document databases, analyzes the research trend of scientific papers and research hot spots. There is practical significance to promote the development of science and technology in China in order to provide effective methods and tools for the researchers. Keywords: Granular Computing, Scientific Papers Research, Research Trend Research Status on Analysis of the Research Trend of the Scientific PaperScientific thesis embodies the research results of various disciplines in the field, is a scientific summary of various theories and methods. With the popularization and ripeness of the internet, the era of the great explosion of the information has already come. With the growing of the number of scientific papers at an alarming rate, that leads to the emergence of various digital libraries and literature database (such as Chinese science paper online). Mining and analysis related to these massive data has been the focus of many scientific researchers.[15] The literature from a variety of database, the analysis of dynamic interdisciplinary science paper changed in each historical period (research trends) over time.At present, it is well known that America ITDT (Interactive Topic Detection and Tracking) is a research project in the field of analysis and discovery [1], in order to deal with the increasingly severe for Internet information explosion problem. The domestic good system is that public sentiment warning assistant decision Support System (ZHISI) in Beijing University FANGZHENG Institute of technology. It successfully realizes automatic and real-time monitoring for Internet public sentiment. This aspect of the theory and technology is mainly applied in the hot news, hot network information finding and tracking, but applied in the analysis of technology research hot spot and trend of research on the less. The Significance of the Research Trend Analysis of Scientific PapersAccording to domestic and foreign literature research, the analysis of the literature research trend mainly based on keywords frequency statistics of literature metrology. The Metrology is an important theoretical tool [16] to analyze the literature trend， which uses mathematical and statistical methods to describe the current situation and development trend, evaluation and forecast of science and technology.At present, the research trends of scientific papers have many problems at home and abroad. It mainly includes the following aspects:1. Somewhat lacking in large-scale data processing; 2. Can not be effective for large-scale literature database processing;

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Section: Research Status On Analysis Of the Research Trend Of The Scimentioning

confidence: 99%

Analysis of Scientific Papers Research Trend Based on Granular Computing

Wang¹,

Yu²,

Li³

2015

IJHIT

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“…The computational expense to execute data or text mining based analysis as advanced services in real-world applications such as digital libraries has been identified as the major cause for the lack of more widespread use of such services [43]. For the easy deployment of text mining applications, a higher level of abstraction is needed than the ones commonly used in HPC.…”

Section: Distributed Computing In a Heterogeneous Environmentmentioning

confidence: 99%

“…This has lead to the development of GPU-based MapReduce models, which makes it easier to adapt algorithms to run on a distributed GPU cluster. A crucial advantage of MapReduce is that it also helps with the integration of other tasks, which in turn aids the development of advanced services, for instance, in digital libraries [43].…”

Section: Introductionmentioning

confidence: 99%

Accelerating text mining workloads in a MapReduce-based distributed GPU environment

Wittek

Daranyi

2013

Journal of Parallel and Distributed Computing

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Scientific computations have been using GPU-enabled computers successfully, often relying on distributed nodes to overcome the limitations of device memory. Only a handful of text mining applications benefit from such infrastructure. Since the initial steps of text mining are typically data-intensive, and the ease of deployment of algorithms is an important factor in developing advanced applications, we introduce a flexible, distributed, MapReducebased text mining workflow that performs I/O-bound operations on CPUs with industry-standard tools and then runs compute-bound operations on GPUs which are optimized to ensure coalesced memory access and effective use of shared memory. We have performed extensive tests of our algorithms on a cluster of eight nodes with two NVidia Tesla M2050 attached to each, and we achieve considerable speedups for random projection and self-organizing maps.

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“…From human being perspective, such research assumes a particular interest when the involved data are natural language documents and the relationships are defined between entities described in text, e.g. [23,24,34,47].…”

Section: Introductionmentioning

confidence: 99%

Supervised semantic relation mining from linguistically noisy text documents

Giannone

Basili

Naggar

et al. 2010

IJDAR

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In this paper, we present models for mining text relations between named entities, which can deal with data highly affected by linguistic noise. Our models are made robust by: (a) the exploitation of state-of-the-art statistical algorithms such as support vector machines (SVMs) along with effective and versatile pattern mining methods, e.g. word sequence kernels; (b) the design of specific features capable of capturing long distance relationships; and (c) the use of domain prior knowledge in the form of ontological constraints, e.g. bounds on the type of relation arguments given by the semantic categories of the involved entities. This property allows for keeping small the training data required by SVMs and consequently lowering the system design costs. We empirically tested our hybrid model in the very complex domain of business intelligence, where the textual data are constituted by reports on investigations into criminal enterprises based on police interrogatory reports, electronic eavesdropping and wiretaps. The target relations are typically established between entities, as they are mentioned in these information sources. The experiments on mining such relations show that our approach with small training data C. Giannone (B) · P. Naggar CM Sistemi s.p

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Integrating data and text mining processes for digital library applications

Cited by 8 publications

References 9 publications

Analysis of Scientific Papers Research Trend Based on Granular Computing

Analysis of Scientific Papers Research Trend Based on Granular Computing

Accelerating text mining workloads in a MapReduce-based distributed GPU environment

Supervised semantic relation mining from linguistically noisy text documents

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