Unformatted, Certified Scientific Objects We present an approach for scientific data management systems to apply certificates to scientific objects, which are typically unformatted datasets, to facilitate analysis by climate scientists. Typically, for a program to process data, the program requires cleansed data in a form that supports automatic manipulation. Most systems require that data must adhere to a specific format to achieve that goal. The technique described in this paper takes the opposite approach; instead, any dataset may be imported and manipulated in the system. But upon initial import, however, only a subset of system functions may work with any given dataset. As the data is refined and transformed by system functions, more functions may become compatible. Certificates are associated with objects that pass constraint validation within the system to ensure that they conform to function requirements. The attached object constraints represent invariant properties of the object, which may be used by functions in the system as function preconditions. Furthermore, the functions defined in the system may associate certificates with the newly generated results. Certificates related to function results are effectively function postconditions, which in turn are used to associate certificates with the objects generated in the system. Additionally, attached object certificates reflect the refinement of data into a more pristine version. This paper describes the technique for modeling and enforcing the constraints for data scientists that have similar requirements.

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.