A Knowledge Acquisition Method for Improving Data Quality in Services Engagements

Dani, Mohan N.; Faruquie, Tanveer A.; Rishabh, Garg,; Kothari, Govind; Mohania, Mukesh; Prasad, Kalika; Subramaniam, L. Venkata; Swamy, Varsha N.

doi:10.1109/scc.2010.91

Cited by 16 publications

(17 citation statements)

References 7 publications

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“…One can request resources as virtual machines with desired configurations running over a xen virtualization platform. The data processing task was cleansing [12] and standardization [5] of one million address records on the cloud platform for a Master Data Management (MDM) implementation. The chosen task is only indicative and can be replaced with any other task.…”

Section: A Data Processing Resultsmentioning

confidence: 99%

“…Not much attention has been given to resource provisioning for such real time data processing requirements in cloud computing literature. Various applications such as data cleansing [5], data transformation, data analysis [14] and data manipulation are offered as services over cloud [8]. All these applications are data intensive and involve transferring of data in real time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Resource Allocation and SLA Determination for Large Data Processing Services over Cloud

Prasad

Faruquie

Subramaniam

et al. 2010

2010 IEEE International Conference on Services Computing

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Data processing on the cloud is increasingly used for offering cost effective services. In this paper, we present a method for resource allocation for data processing services over the cloud taking into account not just the processing power and memory requirements, but the network speed, reliability and data throughput. We also present algorithms for partitioning data, for doing parallel block data transfer to achieve better throughput and allocated cloud resources. We also present methods for optimal pricing and determination of Service Level Agreements for a given data processing job. The usefulness of our approach is shown through experiments performed under different resource allocation conditions.

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Section: A Data Processing Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Resource Allocation and SLA Determination for Large Data Processing Services over Cloud

Prasad

Faruquie

Subramaniam

et al. 2010

2010 IEEE International Conference on Services Computing

Self Cite

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“…A very limited number of papers (Faruquie et al, 2010;Dani et al, 2010) proposes a cloud-based solution to perform data quality improvement activities. A cloud infrastructure to offer virtualized data cleansing that can be accessed as a transient service is presented in (Faruquie et al, 2010).…”

Section: Related Workmentioning

confidence: 99%

“…Setting up data cleansing as a transient service gives rise to several challenges such as (i) defining a dynamic infrastructure for the cleansing on demand based on customer requirements and (ii) defining data transfer and access that meet required service level agreements in terms of data privacy, security, network bandwidth and throughput. Moreover, as further discussed in (Dani et al, 2010), offering data cleansing as a service is a challenge because of the need to customize the rules to be applied for different datasets. The Ripple Down Rules (RDR) framework is proposed in (Dani et al, 2010) to lower the manual effort required in rewriting the rules from one source to another.…”

Section: Related Workmentioning

confidence: 99%

A Cloud-Based Solution for Data Quality Improvement

Comerio

2012

Proceedings of the 2nd International Conference on Cloud Computing and Services Science

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The application of techniques to improve the data quality of an organization is traditionally costly since different specific tools are required. Potentially, cloud computing models could offer powerful solutions to reduce costs. However, some challenges remain in the widespread acceptance of cloud computing models because they require the sharing of business critical data. Therefore, services for data quality improvements in the cloud should act in compliance with predefined contracts. This paper extends previous works on the specification, selection and evaluation of service and data contracts. Moreover, a cloud-based architecture for data quality improvement that supports contract-based service selection is proposed. Experimental activities on a real scenario demonstrate the feasibility of the proposed solution.

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“…for redeveloping legacy systems. Other companies offer RDR products as one of their range, e.g an RDR data cleansing product from IBM 4 [10]. These examples are included to illustrate that RDR are used in other areas beyond medical diagnostic reporting which is the source of the log data described here.…”

Section: Ripple-down Rules (Rdr)mentioning

confidence: 99%

Experience with long-term knowledge acquisition

Compton

Peters²,

Lavers³

et al. 2011

Proceedings of the Sixth International Conference on Knowledge Capture

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Evaluation has remained a major challenge for knowledge acquisition and little data is available on how experts actually use knowledge acquisition technology. A number of companies offer Ripple-Down Rules to enable on-going knowledge acquisition and maintenance while a system is in use. One of these companies, Pacific Knowledge Systems 1 has logged user activity over a number of years. Data from these logs demonstrate that domain experts continue to add knowledge to a knowledge base over years. The logs also demonstrate that new knowledge can be added very rapidly regardless of knowledge base size or age. We assume that the on-going knowledge acquisition observed was driven by the need to make changes and encouraged and allowed by the ease of the knowledge acquisition technology used. The question arises of whether experts in other domains would also chose to continue to add knowledge to their knowledge bases if this was supported.

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A Knowledge Acquisition Method for Improving Data Quality in Services Engagements

Cited by 16 publications

References 7 publications

Resource Allocation and SLA Determination for Large Data Processing Services over Cloud

Resource Allocation and SLA Determination for Large Data Processing Services over Cloud

A Cloud-Based Solution for Data Quality Improvement

Experience with long-term knowledge acquisition

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