A Component-Based Data Management and Knowledge Discovery Framework for Aviation Studies

Blake, M. Brian; Singh, Lisa; Williams, Andrew B.; Norman, Wendell; Sliva, Amy

doi:10.4018/9781605660943.ch006

Cited by 3 publications

(5 citation statements)

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“…In addition, this is perhaps the first general-purpose framework that exploits KDD in the aviation domain. This paper presents an experience report of the C-KDD, but in-depth technical details can be found in related literature (Blake, 2005).…”

Section: Discussionmentioning

confidence: 99%

“…Other attributes allow the user to specify a zone by number of columns. It is not in the scope of this paper to describe the templating technique in detail but technical details can be found in related work (Blake, 2005).…”

Section: E Xml and Html)mentioning

confidence: 99%

“…Several MITRE analysts were consulted to confirm that the results were consistent with the trends of weather-impacted operations at the facilities. The specific aviation-based results of the studies can be found in related literature (Blake, 2005).…”

Section: Verifying C-kdd By Regenerating Known Aviation Rulesmentioning

confidence: 99%

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Experience Report

Blake

Singh

Williams

et al. 2006

International Journal of Information Technology and Web Engineering

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Organizations are beginning to apply data mining and knowledge discovery techniques to their corporate data sets, thereby enabling the identification of trends and the discovery of inductive knowledge. Many times, traditional transactional databases are not optimized for analytical processing and must be transformed. This article proposes the use of modular components to decrease the overall amount of human processing and intervention necessary for the transformation process. Our approach configures components to extract data-sets using a set of "extraction hints". Our framework incorporates decentralized, generic components that are reusable across domains and databases. Finally, we detail an implementation of our component-based framework for an aviation data set.

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Section: Discussionmentioning

confidence: 99%

Section: E Xml and Html)mentioning

confidence: 99%

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Experience Report

Blake

Singh

Williams

et al. 2006

International Journal of Information Technology and Web Engineering

Self Cite

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“…Moreover, these small components can be replaced (or updated) in the future by only changing respective components of the management system while keeping the rest of the system unchanged. A number of research groups have utilized component-based management approaches to develop performance management frameworks for different application domains, such as web services [11], multimedia applications [12], network management [13], sensor network [14], real-time systems [15], and workflow systems [16]. These existing component-based approaches are applicable to only specific type of applications.…”

Section: B Component-based Performance Management Solutionsmentioning

confidence: 99%

A component-based framework for autonomic performance management in a distributed computing environment

Mehrotra

Abdelwahedy

2014

2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA)

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Distributed computing systems (DCS) host a wide variety of enterprise applications in dynamic and uncertain operating environments. These applications require stringent reliability, availability, and quality of service (QoS) guarantee to maintain their service level agreements (SLAs). Due to the growing size, increasing complexity, and varying nature of applications hosted in DCS, development of a single autonomic performance management system is difficult to maintain the SLAs of all of these applications. Therefore, a customizable autonomic performance management system is introduced in this paper, by using model-integrated computing methodologies, which allow application domain architects to develop meta-models of each system, various system modules with attributes, their connectivity, constraints, and visualization aspects. Then, domain engineers can define the initial settings of the application, QoS objectives, system components' placement, and interaction among these components in a graphical domain specific modeling environment. This configurable performance management system facilitates reusability of the same components, algorithms, and application performance models in different deployment settings. I. INTRODUCTIONDistributed computing systems have been used for hosting e-commerce, social networking, internet search services, and information broadcasting applications during the past decades. The growing use of applications, hosted in distributed computing systems, increases the demand to ensure the availability, the reliability, and the quality of service (QoS) in dynamic and uncertain operating environments. Effective management in such operating environments requires expert administrator knowledge to determine the capacity requirement and resource allocation based on incoming workload pattern and application behavior; however, manual administration leads to poor performance and frequent outages due to the extremely complex application dynamics and the large size of the deployment.Another major challenge in distributed computing systems is the wide variety of hosted applications, such as stock trading, medical infrastructure, news portal, media streaming, and storage services. These applications are different from each other in the deployment architecture (data center or cloud environment), environment inputs (session-based, session-less, or different arrival pattern), monitored components (software or hardware), system performance model, and QoS requirements. Moreover, these applications are deployed with different operational settings and placement strategies. Therefore, the development of a single performance management system to maintain the QoS of each of these applications in different deployment schemes is infeasible.

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“…That is to search for relationships and global patterns that exist in large databases but are hidden in the vast amounts of data, which might take years to find with conventional techniques [18]. The significant development in database and software technologies, i.e., warehousing of transaction data has enabled the organisations to build the foundation for knowledge discovery in databases (KDD) which consists of such phases as selection, preprocessing, transformation, data mining and interpretation/evaluation [1].…”

Section: Data Miningmentioning

confidence: 99%

Data Refining for Text Mining Process in Aviation Safety Data

Sjöblom

2009

Software Services for E-Business and E-Society

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Abstract. Successful data mining is an iterative process during which data will be refined and adjusted to achieve more accurate mining results. Most important tools in the text mining context are list of stop words and list of synonyms. The size and richness of the lists mentioned depend on the structure of the language used in the text to be mined. English, for example, is an "easy" language for search technologies, because with a couple of exceptions, the stem of the word is not conjugated and terms are formed using several words instead of creating compounds. This requires special attention to definitions when processing morphologically rich languages like Finnish. This chapter introduces the need and realisation of refining the source data for a successful data mining process based onto the results achieved from first mining round.

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A Component-Based Data Management and Knowledge Discovery Framework for Aviation Studies

Cited by 3 publications

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Experience Report

Experience Report

A component-based framework for autonomic performance management in a distributed computing environment

Data Refining for Text Mining Process in Aviation Safety Data

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