The Big Data revolution has promised to build a data-driven ecosystem where better decisions are supported by enhanced analytics and data management. However, critical issues still need to be solved in the road that leads to commodization of Big Data Analytics, such as the management of Big Data complexity and the protection of data security and privacy. In this paper, we focus on the first issue and propose a methodology based on Model Driven Engineering (MDE) that aims to substantially lower the amount of competences needed in the management of a Big Data pipeline and to support automation of Big Data analytics. The proposal is experimentally evaluated in a real-world scenario: the implementation of novel functionality for Threat Detection Systems.
Big Data technology has discarded traditional data modelling approaches as no longer applicable to distributed data processing. It is, however, largely recognised that Big Data impose novel challenges in data and infrastructure management. Indeed, multiple components and procedures must be coordinated to ensure a high level of data quality and accessibility for the application layers, e.g. data analytics and reporting. In this paper, the third of its kind co-authored by mem-
Big Data domain is one of the most promising ICT sectors with substantial expectations both on the side of market growing and design shift in the area of data storage managment and analytics. However, today, the level of complexity achieved and the lack of standardisation of Big Data management architectures represent a huge barrier towards the adoption and execution of analytics especially for those organizations and SMEs not including a sufficient amount of competences and knowledge. The full potential of Big Data Analytics (BDA) can be unleashed only through the definition of approaches that accomplish Big Data users' expectations and requirements, also when the latter are fuzzy and ambiguous. Under these premises, we propose Big Data Analytics-as-a-Service (BDAaaS) as the next-generation Big Data Analytics paradigm and we discuss issues and challenges from the BDAaaS design and development perspective.
Online process mining refers to a class of techniques for analyzing in real-time event streams generated by the execution 5 of business processes. These techniques are crucial in the reactive monitoring of business processes, timely resource allocation and 6 detection/prevention of dysfunctional behavior. Many interesting advances have been made by the research community in recent 7 years, but there is no consensus on the exact set of properties these techniques have to achieve. This article fills the gap by identifying 8 a set of evaluation goals for online process mining and examining their fulfillment in the state of the art. We discuss parameters and 9 techniques regulating the balance between conflicting goals and outline research needed for their improvement. Concept drift detection 10 is crucial in this sense but, as demonstrated by our experiments, it is only partially supported by current solutions. Q1 11 Index Terms-Online process mining, event stream, requirements and goals, concept drift Ç 12 1 INTRODUCTION 13 P ROCESS Mining (PM) is a set of data science techniques 14 focused on the analysis of event logs [1]. Events are 15 recorded when executing a Business Process and collected 16 into cases, i.e., end to end sequences of events relevant to the 17 same process instance. Traditional PM algorithms were 18 designed to work offline, analyzing historical batches of logs 19 gathering the complete course of cases, if necessary with 20 multiple passes of analysis. This is, however, insufficient, 21 from a business standpoint, when the real-time assessment 22 of processes is crucial to timely manage resources and 23 quickly react to dysfunctional behaviors [2]. Today's fast-24 changing market requires systematic adjustments of pro-25 cesses in response to changes in the organization's operat-26 ing system or to trends emerging from the environment [3]. 27 Recently, the notion of online PM has emerged in reference 28 to analytics capable of handling real-time event streams [4], 29 [5]. An event stream differs from an event log because it is an 30 unbounded sequence of events ingested one-by-one and 31 allowing for limited actions in terms of iteration and memory 32 or time consumption [6].
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