ImpSlidingWindow: A New Model to Improve the Performance of the Sliding Window Based Streaming Data Summarization Method

Year 2019, Special Issue 2019, 292 - 301, 31.10.2019

Ali Şenol , Hacer Karacan

https://doi.org/10.31590/ejosat.638096

Abstract

Sliding window based data summarization which is a quantity based summarization is commonly used in data stream clustering area in which the recent data is more important. In this data summarization method, w which is a predefined variable, of the most recent data is taken as the summary each time a new data arrives and the window slides one by one. This means that the model processes all the data in the data window each time a new data arrives. This approach causes the performance to reduce. Therefore, there is a need of new studies to be proposed in this area. In this study, a new sliding window model named ImpSlidingWindow (ISW) is proposed as a solution to the mentioned problem. In the proposed model, we propose that clustering model to work whenever a certain number of data accumulates instead of each data entry. With this new model, the sliding window width is divided into four equal parts and the clustering model works at the end of each part. As a result, a significant increase in the performance is achieved by enabling the clustering model to run four times instead of working as much as the number of data in the window width. When the proposed model applied to KD-AR Stream algorithm which is a proposed algorithm in the data stream clustering area, it has been found that up to 80% improvement obtained in run-time complexity.

Keywords

Data stream clustering, data summarization, sliding window

ImpSlidingWindow: Kayan Pencere Tabanlı Akan Veri Özetleme Yönteminin Performansını Arttırmaya Yönelik Yeni Bir Model

Abstract

Kayan pencere tabanlı veri özetleme, akan veri kümeleme alanında son gelen verilerin daha önemli olduğu uygulamalarda sıkça kullanılan miktar tabanlı bir veri özetleme yaklaşımıdır. Bu veri özetleme yaklaşımında, her yeni veri gelişinde ön tanımlı bir değişken olan en son gelen w tane veri özet olarak alınır ve pencere birer birer kaymaktadır. Yani model her yeni veri girişinde veri penceresinde bulunan tüm verileri işler. Bu da performansı olumsuz etkilemektedir. Bu nedenle bu probleme çözüm üretecek çalışmalara ihtiyaç duyulmaktadır. Bu çalışmada sözü edilen probleme çözüm olarak ImpSlidingWindow (ISW) isimli yeni bir kayan pencere modeli önerilmektedir. Önerilen modelde her veri girişinde kümeleme modelinin çalışması yerine belirli sayıda veri biriktikçe kümeleme modelinin çalışması önerilmektedir. Bu yeni model ile kayan pencere genişliği dört eşit parçaya bölünmekte ve her parçanın sonunda kümeleme modelinin çalışması sağlanmaktadır. Sonuç olarak pencere genişliğinde bulunan veri sayısı kadar kümeleme modelinin çalışması yerine dört defa çalışması sağlanarak performansta çok önemli bir artış sağlanmaktadır. Önerilen model akan veri kümeleme alanında önerilmiş bir algoritma olan KD-AR Stream algoritmasına uygulandığında çalışma zamanında %80’lara varan iyileştirmeler elde edilmiştir.

Keywords

Akan veri kümeleme, veri özetleme, kayan pencereler

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