Smart archive: a component-based data mining application framework

Laurinen, Perttu; Tuovinen, Lauri; Röning, Juha

doi:10.1109/isda.2005.83

Cited by 14 publications

(12 citation statements)

References 10 publications

(12 reference statements)

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“…The implementation was carried out using a software framework called Smart Archive [11] in order to benefit from ready interfaces and advance the reuse of code and designs. The purpose of Smart Archive is to provide general functionality for implementing real-time data mining applications and for utilizing efficiently large amounts of historical measurement data, and thus shorten the time needed to develop tailored data mining applications.…”

Section: Technical Implementationmentioning

confidence: 99%

Semi-automatic Maintenance of Regression Models: an Application in the Steel Industry

Juutilainen

Tuovinen

Laurinen

et al. 2011

CIT

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Software applications used in the controlling and planning of production processes commonly make use of predictive statistical models. Changes in the process involve a more or less regular need for updating the prediction models on which the operational software applications are based. The objective of this article is • to provide information which helps to design semiautomatic systems for the maintenance of statistical prediction models and • to describe a proof-of-concept implementation in an industrial application.The system developed processes the production data and provides an easy-to-use interface to construct updated models and introduce them into a software application. The article presents the architecture of the maintenance system, with a description of the algorithms that cause the system's functionality. The system developed was implemented for keeping up-to-date prediction models which are in everyday use in a steel plate mill in the planning of the mechanical properties of steel products. The conclusion of the results is that the semi-automatic approach proposed is competitive with fully automatic and manual approaches. The benefits include good prediction accuracy and decreased workload of the deployment of updated model versions.

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Section: Technical Implementationmentioning

confidence: 99%

Semi-automatic Maintenance of Regression Models: an Application in the Steel Industry

Juutilainen

Tuovinen

Laurinen

et al. 2011

CIT

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“…DBSA and SA are both derived from the same reference architecture introduced by Laurinen et al [9]. Like DBSA, SA models applications as independent components connected by data flows, using pipes-and-filters architectural patterns to implement the data flows.…”

Section: Motivation and Related Workmentioning

confidence: 99%

“…The reference architecture common to data mining applications is shown in Figure 1 [9]. This architecture can be found in data mining applications implemented with both SA and DBSA, even though the actual system architectures of these two frameworks differ considerably.…”

Section: Description Of the Frameworkmentioning

confidence: 99%

“…The framework is called the Device-Based Software Architecture (DBSA). It is written in C++ and combines features from the pipes-and-filters and layered architectural styles, which are commonly seen in data mining software [9,11,2].…”

Section: Introductionmentioning

confidence: 99%

“…There are currently some versatile data mining frameworks available [9,11,2]. Unfortunately, most of these are either commercial or restricted in platform support.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Se-155 Dbsa

Kätevä

Laurinen

Rautio

et al. 2010

Proceedings of the 2010 ACM Symposium on Applied Computing

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In this paper a new architecture for a variety of data mining tasks is introduced. The Device-Based Software Architecture (DBSA) is a highly portable and generic data mining software framework where processing tasks are modeled as components linked together to form a data mining application. The name of the architecture comes from the analogy that each processing task in the framework can be thought of as a device. The framework handles all the devices in the same manner, regardless of whether they have a counterpart in the real world or whether they are just logical devices inside the framework. The DBSA offers many reusable devices, ready to be included in applications, and the application programmer can easily code new devices for the architecture. The framework is bundled with connections to several widely used external tools and languages, making prototyping new applications easy and fast. In the paper we compare DBSA to existing data mining frameworks, review its design and present a case study application implemented with the framework. The paper shows that the DBSA can act as a base for diverse data mining applications.

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