A first tutorial on dataspaces

Franklin, Michael J.; Halevy, Alon; Maier, David

doi:10.14778/1454159.1454217

Cited by 23 publications

(15 citation statements)

References 10 publications

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“…For example, the simple services such as keyword search are firstly offered by DSMS over a collection of data even before semantic mappings are created, and then the besteffort or approximate answers are return to users. Over time, where and when to invest more efforts (e.g., semantic mappings creation and other kind of semantic constructs) to improve the accuracy of query results would be determined by users or database administrators, whenever they want to obtain the higher level of services [10].…”

Section: B Best-effort Querymentioning

confidence: 99%

A Pay-as-You-Go Framework for Data Management

Pan

Tang

2011

2011 International Conference on Computational and Information Sciences

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Extensive effort has been brought forth to assist in data management. In particular, classic information integration systems and database systems techniques are exploited to manage evolving data. However, these works require hard up-front investment before offering powerful functionalities. To address the question, dataspace, a new style of data management, was proposed in 2005 SIGMOD. In this paper, a general framework based on dataspace techniques is presented to manage data in a pay-as-you-go fashion, and the core components of the framework are specifically explained. Moreover, the differences between pay-as-you-go fashion and pay-before-you-go fashion in data management are discussed.

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Section: B Best-effort Querymentioning

confidence: 99%

A Pay-as-You-Go Framework for Data Management

Pan

Tang

2011

2011 International Conference on Computational and Information Sciences

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“…2) Pay-as-you-go data management: In sharp contrast to existing information integration systems and database systems, Dataspaces emphasizes the idea of pay-as-you-go data management: It offers services on data without requiring the upfront long setup time, and improves the services as more investment is made into identifying semantic relationships. [10] [11] 3) Support for all different structure and distributed artifact: current traceability methods have been developed to trace only well structured data [6]. How to handle semi-structured, unstructured artifacts and distributed data is still a research challenge.…”

Section: Dataspacementioning

confidence: 99%

Software Artifacts Management Based on Dataspace

Pan

Tang

2009

2009 WASE International Conference on Information Engineering

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Establish and maintain software artifact traceability is widely recognized as one of the major challenges in software engineering research. Because of the lack of automatic or semi-automatic methodologies and technologies, traceability information is often out of date or completely missing as the software changes. We present a framework for software artifacts management based on dataspace. The characteristics of dataspace are extremely advantageous to manage the lifecycle of software artifacts, support all different artifact structure, and support automatic or semi-automatic traceability recovery and maintenance. Moreover, utilizing the existing software engineering ontologies, the system can "understand" software artifacts and traceability among them more easily.

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“…Franklin et al [1] propose the concept of Dataspace for managing collection of heterogeneous data. A Dataspace integrates its data in an incremental fashion and improves its performance with time [1,2,6]. The Dataspace technology is in its development phase.…”

Section: Introductionmentioning

confidence: 99%

Comparison of ranking algorithms with dataspace

Lal

Qamar

2015

2015 International Conference on Advances in Computer Engineering and Applications

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With increased in digitization the amount of homogeneous, unstructured, semi-structured, structured or heterogeneous data being created and stored is exploding is collectively called "Dataspace". Data being generated from various heterogeneous sources like, digital images, audio, video , online transactions, online social media , data from sensor nodes , click streams for different domains including, retails, medical , healthcare , energy, and day to day life utilities. In business, industries, institutions and organizations, individuals contribute the data volume like technical reports, seminar reports, research papers, dissertations, thesis etc. For instance, 30 billion web pages are accessed or the World Wide Web. With terrific number of pages of that exist today; search engines assume a significant role in the current internet of thing (IOT). So with billions of web pages accessible on the web, a user query entered in the search engine may returns thousands of web pages, and thus it becomes extremely important to rank these results in such a way that the most "related" or "important" or "authorized" pages are displayed first. This job of prioritizing the results is performed by ranking algorithms, and various search engines use different schemes for ranking the results. Ranking of data can also do in heterogeneous data to retrieve information from the Dataspace. The aim of this paper is to describe Dataspace and present a survey on ranking algorithms, and their comparison, Comparison is done on the basis of some parameters such as main technique use, methodology, and input parameter, and relevancy, quality of results, importance and limitations, search engines and time complexity of algorithms. In this we also explained how ranking can be used in Dataspace with challenges to information retrieval from heterogeneous data or from Dataspace.

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A first tutorial on dataspaces

Cited by 23 publications

References 10 publications

A Pay-as-You-Go Framework for Data Management

A Pay-as-You-Go Framework for Data Management

Software Artifacts Management Based on Dataspace

Comparison of ranking algorithms with dataspace

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