Introduction to “This is Watson”

Ferrucci, David

doi:10.1147/jrd.2012.2184356

Cited by 352 publications

(183 citation statements)

References 27 publications

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“…The grand challenge presented to, and addressed by, the IBM Watson project demonstrated the feasibility of co-processing, using machine learning techniques, structured and unstructured data in order to answer questions posed while playing the television game show Jeopardy [2][3][4][5][6]. This complex task of question disambiguation, fact identification, and analysis provided the general public with a concrete example of how machines can be used to answer fact-based questions.…”

Section: Figure 1 the Five Vs Of Big Data The Questionsmentioning

confidence: 99%

Developing an Open Source 'Big Data' Cognitive Computing Platform

Kowolenko¹,

Vouk²

2018

Ubiquity

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Section: Figure 1 the Five Vs Of Big Data The Questionsmentioning

confidence: 99%

Developing an Open Source 'Big Data' Cognitive Computing Platform

Kowolenko¹,

Vouk²

2018

Ubiquity

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“…Details on any errors. 3. Performance information like computational breakdown, swapping, garbage collection and CPU utilization. "…”

Section: B Archiving the Visualizationmentioning

confidence: 99%

“…In the past decade, Web companies such as Google, Yahoo, Amazon and IBM have pioneered new data processing platforms, such as MapReduce [1,2] and Watson [3], which scale to clusters of thousands of servers and have a capacity of many petabytes. In this paper we will describe a new visualization tool that is generic enough to work with several such platforms.…”

Section: Introductionmentioning

confidence: 99%

Visualizing jobs with shared resources in distributed environments

Pauw¹,

Wolf²,

Balmin

2013

2013 First IEEE Working Conference on Software Visualization (VISSOFT)

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In this paper we describe a visualization system that shows the behavior of jobs in large, distributed computing clusters. The system has been in use for two years, and is sufficiently generic to be applied in two quite different domains: a Hadoop MapReduce environment and the Watson DeepQA DUCC cluster. Scalable and flexible data processing systems typically run hundreds or more of simultaneous jobs. The creation, termination, expansion and contraction of these jobs can be very dynamic and transient, and it is difficult to understand this behavior without showing its evolution over time. While traditional monitoring tools typically show either snapshots of the current load balancing or aggregate trends over time, our new visualization technique shows the behavior of each of the jobs over time in the context of the cluster, and in either a real-time or post-mortem view. Its new algorithm runs in realtime mode and can make retroactive adjustments to produce smooth layouts. Moreover, our system allows users to drill down to see details about individual jobs. The visualization has been proven useful for administrators to see the overall occupancy, trends and job allocations in the cluster, and for users to spot errors or to monitor how many resources are given to their jobs.

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“…Watson IBM Watson [42] is a Question and Answering system capable of parsing, understanding and answering queries provided in natural language. IBM Watson utilized some pieces of information available as a part of DBpedia dataset.…”

Section: Applicationsmentioning

confidence: 99%

Linked open data alignment & querying

Jain

2013

SIGWEB Newsl.

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Linked Open Data Alignment & Querying.The recent emergence of the Linked Data approach for publishing data represents a major step forward in realizing the original vision of a web that can "understand and satisfy the requests of people and machines to use the web content" i.e. the Semantic Web. This new approach has resulted in the Linked Open Data (LOD) Cloud, which includes more than 295 large datasets contributed by experts belonging to diverse communities such as geography, entertainment, and life sciences. However, the current interlinks between datasets in the LOD Cloud as we will illustrate are too shallow to realize much of the benefits promised. If this limitation is left unaddressed, then the LOD Cloud will merely be more data that suffers from the same kinds of problems, which plague the Web of Documents, and hence the vision of the Semantic Web will fall short.This thesis presents a comprehensive solution to address the issue of alignment and relationship identification using a bootstrapping based approach. By alignment we mean the process of determining correspondences between classes and properties of ontologies. We identify subsumption, equivalence and part-of relationship between classes. The work identifies part-of relationship between instances. Between properties we will establish subsumption and equivalence relationship. By bootstrapping we mean the process of being able to utilize the information which is contained within the datasets for improving the data within them. The work showcases use of bootstrapping based methods to identify and create richer relationships between LOD datasets. The BLOOMS project (http://wiki.knoesis.org/index.php/BLOOMS) and the PLATO project, both built as part of this research, have provided evidence to the feasibility and the applicability of the solution.

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Introduction to “This is Watson”

Cited by 352 publications

References 27 publications

Developing an Open Source 'Big Data' Cognitive Computing Platform

Developing an Open Source 'Big Data' Cognitive Computing Platform

Visualizing jobs with shared resources in distributed environments

Linked open data alignment & querying

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