Data Mining Practical Machine Learning Tools and Techniques

An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.

This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.

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Opinion Mining and Sentiment Analysis

How to Do Things With Words

語用論(Pragmatics)を考える

This paper describes the Pegasus framework that can be used to map complex scientific workflows onto distributed resources. Pegasus enables users to represent the workflows at an abstract level without needing to worry about the particulars of the target execution systems. The paper describes general issues in mapping applications and the functionality of Pegasus. We present the results of improving application performance through workflow restructuring which clusters multiple tasks in a workflow into single entities. A real-life astronomy application is used as the basis for the study.

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Pegasus: A framework for mapping complex scientific workflows onto distributed systems

Describes the Wings intelligent workflow system that assists scientists with designing computational experiments by automatically tracking constraints and ruling out invalid designs, letting scientists focus on their experiments and goals.

Wings: Intelligent Workflow-Based Design of Computational Experiments

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뀀Ƞ WINGS:뀀ȠIntelligent뀀ȠWorkflow-Based뀀ȠDesign뀀Ƞ뀀Ƞ of뀀ȠComputational뀀ȠExperiments뀀Ƞ

Scientific workflows are being developed for many domains as a useful paradigm to manage complex scientific computations. In our work, we are challenged with efficiently generating and validating workflows that contain large amounts (hundreds to thousands) of individual computations to be executed over distributed environments. This paper describes a new approach to workflow creation that uses semantic representations to describe compactly complex scientific applications in a dataindependent manner, then automatically generates workflows of computations for given data sets, and finally maps them to available computing resources. The semantic representations are used to automatically generate descriptions for each of the thousands of new data products. We interleave the creation of the workflow with its execution, which allows intermediate execution data products to influence the generation of the following portions of the workflow. We have implemented this approach in Wings, a workflow creation system that combines semantic representations with planning techniques. We have used Wings to create workflows of thousands of computations, which are submitted to the Pegasus mapping system for execution over distributed computing environments. We show results on an earthquake simulation workflow that was automatically created with a total number of 24,135 jobs and that executed for a total of 1.9 CPU years.

Wings for Pegasus: creating large-scale scientific applications using semantic representations of computational workflows

Our research focuses on creating and executing large-scale scientific workflows that often involve thousands of computations over distributed, shared resources. We describe an approach to workflow creation and refinement that uses semantic representations to (1) describe complex scientific applications in a data-independent manner, (2) automatically generate workflows of computations for given data sets, and (3) map the workflows to available computing resources for efficient execution. Our approach is implemented in the Wings-Pegasus workflow system and has been demonstrated in a variety of scientific application domains. This paper illustrates the application-level provenance information generated Wings during workflow creation and the refinement provenance by the Pegasus mapping system for execution over grid computing environments. We show how this information is used in answering the queries of the First Provenance Challenge. Copyright © 2007 John Wiley & Sons, Ltd.

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Provenance trails in the Wings-Pegasus system

This paper describes a discussion-bot that provides answers to students' discussion board questions in an unobtrusive and human-like way. Using information retrieval and natural language processing techniques, the discussion-bot identifies the questioner's interest, mines suitable answers from an annotated corpus of 1236 archived threaded discussions and 279 course documents and chooses an appropriate response. A novel modeling approach was designed for the analysis of archived threaded discussions to facilitate answer extraction. We compare a self-out and an all-in evaluation of the mined answers. The results show that the discussion-bot can begin to meet students' learning requests. We discuss directions that might be taken to increase the effectiveness of the question matching and answer extraction algorithms. The research takes place in the context of an undergraduate computer science course.

Jihie Kim

Papers

Wings: Intelligent Workflow-Based Design of Computational Experiments

뀀Ƞ WINGS:뀀ȠIntelligent뀀ȠWorkflow-Based뀀ȠDesign뀀Ƞ뀀Ƞ of뀀ȠComputational뀀ȠExperiments뀀Ƞ

Wings for Pegasus: creating large-scale scientific applications using semantic representations of computational workflows

Provenance trails in the Wings-Pegasus system

An intelligent discussion-bot for answering student queries in threaded discussions