A recommendation system for allocating video resources in multiple partitions

Abstract

A recommendation system or recommender aims to deliver meaningful recommendations for items or services to any interested party (e.g., users and applications). Recommenders provide their results on top of the collected data related either to the items’ and users’ description or ratings defined by users. Recommenders can be adopted in the domain of large-scale data management with significant advantages. Due to huge volumes of data, many techniques consider the separation of data into a number of partitions. Analytics are delivered on top of these data partitions and, accordingly, are aggregated to form the final response into the incoming queries. Data separation techniques can be incorporated to allocate the data into the appropriate partitions, thus, to improve the efficiency in the delivery of analytics. In this chapter, we propose a recommendation system responsible for allocating the data to the most appropriate partition according to their current contents. Our approach facilitates the provision of the analytics for each data partition by collecting “similar” data into the same partition. The aim is to support statistical insights into every partition to efficiently define query execution plans. We adopt a decision-making scheme combined with a naïve Bayesian classifier for deriving the appropriate partition. We focus on the management of streams of video files. The proposed recommender derives the appropriate partition for each incoming video file based on a set of characteristics. We evaluate our scheme through a set of simulations that reveal its strengths and weaknesses. © The Institution of Engineering and Technology 2020.