Grant: Collaborative Research and Development Grants
Queen's University
$69,000.00
Analytics-as-a-service workflow orchestration
FY 2014-15
Summary
The explosion of data now available from both private and public data sources means analytics technologies that mine structured and unstructured data to derive insights to improve decision-making are receiving substantial attention. The traditional data analytics architecture consists of a data warehouse that consolidates data using data integration processes from several operational sources and provides querying, reporting, and analytical tools. Data integration is a batch process implemented by Extract-Transform-Load (ETL) tools. However, as organizations become more automated, real-time and data-driven, this architecture cannot satisfy the enterprises' need for real-time data analytics. The Analytics-as-a-Service (AaaS) model supports the wide adoption of analytics technologies by leveraging cloud computing platforms. AaaS is intended to support a potentially large number of concurrent users and to deliver highly customizable analytics services such that different consumers can create their own workflows involving common analytics engines and products. Since AaaS on cloud platforms does not yet exist, our research presents a unique opportunity to place Canadian researchers and the ITC industry sector partner at the forefront of this emerging opportunity. Workflow orchestration, which is the activity of defining the sequence of tasks needed to manage a process, is fundamental to AaaS. Analytic workflows are data-centric in that they specify the flow of data from one or more data sources through a series of processing steps to a final report or visualization for the user. The goal of the proposed research is to develop a workflow orchestration framework for AaaS that will allow users to perform new and complex data analysis by composing multiple analytic products into an executable workflow. We will produce new techniques and tools to advance software products servicing the important and growing data analytics market. We will also provide valuable training for six HQP.
Program
Collaborative Research and Development Grants
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
453459-2013
Name nom
Martin, Patrick
Department département
Computing, School of
Organization id
29
Institution établissement
Queen's University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2014
Competition year année de concours
2013
Award amount
69000
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
802
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Information systems and technology
Area of application fr
Systèmes d'information et technologie de l'information
Research subject code
2700
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Information technology
Research subject fr
Technologie de l'information
Partie
2
Nb partie
3
Application title
Analytics-as-a-service workflow orchestration
Keyword
NSERC
Application summary
The explosion of data now available from both private and public data sources means analytics technologies that mine structured and unstructured data to derive insights to improve decision-making are receiving substantial attention. The traditional data analytics architecture consists of a data warehouse that consolidates data using data integration processes from several operational sources and provides querying, reporting, and analytical tools. Data integration is a batch process implemented by Extract-Transform-Load (ETL) tools. However, as organizations become more automated, real-time and data-driven, this architecture cannot satisfy the enterprises' need for real-time data analytics. The Analytics-as-a-Service (AaaS) model supports the wide adoption of analytics technologies by leveraging cloud computing platforms. AaaS is intended to support a potentially large number of concurrent users and to deliver highly customizable analytics services such that different consumers can create their own workflows involving common analytics engines and products. Since AaaS on cloud platforms does not yet exist, our research presents a unique opportunity to place Canadian researchers and the ITC industry sector partner at the forefront of this emerging opportunity. Workflow orchestration, which is the activity of defining the sequence of tasks needed to manage a process, is fundamental to AaaS. Analytic workflows are data-centric in that they specify the flow of data from one or more data sources through a series of processing steps to a final report or visualization for the user. The goal of the proposed research is to develop a workflow orchestration framework for AaaS that will allow users to perform new and complex data analysis by composing multiple analytic products into an executable workflow. We will produce new techniques and tools to advance software products servicing the important and growing data analytics market. We will also provide valuable training for six HQP.
