Usability evaluation of a domainspecific language for defining aggregated processing tasks The effective processing of Big Data sets often requires some programming knowledge from a prospective userxe2x80x99s part. This could prove costly to achieve, in terms of user training time and effort, depending on the level of previous experience. The premise, when dealing with large data sets, is that it should be as easy as possible for a user to prototype and test processing algorithms, in order to deal with them in an effective manner. For this reason, we have developed a domain specific language meant to allow users to define data processing tasks as aggregates, consisting of atomic operations. Its goal is to do away with some of the complexities of traditional programming languages, by simplifying the representation model and providing a more intuitive process description tool for its users. This paper aims to evaluate the efficiency and effectiveness with which a novice user could employ our domainspecific language to define processing tasks, and then compare the results to those obtained while using the Python programming language. The experiments will be focused on task duration, description correctness and code interpretation, highlighting possible advantages and disadvantages observed during the usage of the two languages.

Graphical Flowbased Spark Programming Increased sensing data in the context of the Internet of Things (IoT) necessitates data analytics. It is challenging to write applications for Big Data systems due to complex, highly parallel software frameworks and systems. The inherent complexity in programming Big Data applications is also due to the presence of a wide range of target frameworks, with different data abstractions and APIs. The paper aims to reduce this complexity and its ensued learning curve by enabling domain experts, that are not necessarily skilled Big Data programmers, to develop data analytics applications via domainspecific graphical tools. The approach follows the flowbased programming paradigm used in IoT mashup tools. The paper contributes to these aspects by (i) providing a thorough analysis and classification of the widely used Spark framework and selecting suitable data abstractions and APIs for use in a graphical flowbased programming paradigm and (ii) devising a novel, generic approach for programming Spark from graphical flows that comprises earlystage validation and code generation of Spark applications. Use cases for Spark have been prototyped and evaluated to demonstrate codeabstraction, automatic data abstraction interconversion and automatic generation of target Spark programs, which are the keys to lower the complexity and its ensued learning curve involved in the development of Big Data applications.