Anna Queralt

Artifact-Centric Business Process Models in UML

Business process modeling using an artifact-centric approach has raised a significant interest over the last few years. This approach is usually stated in terms of the BALSA framework which defines the four “dimensions” of an artifact-centric business process model: Business Artifacts, Lifecycles, Services and Associations. One of the research challenges in this area is looking for different diagrams to represent these dimensions. Bearing this in mind, the present paper shows how all the elements in BALSA can be represented by using the UML language. The advantages of using UML are many. First of all, it is a formal language with a precise semantics. Secondly, it is widely used and understandable by both business people and software developers. And, last but not least, UML allows us to provide an artifact-centric specification for BALSA which incorporates also some aspects of process-awareness.

PyCOMPSs: Parallel computational workflows in Python

The use of the Python programming language for scientific computing has been gaining momentum in the last years. The fact that it is compact and readable and its complete set of scientific libraries are two important characteristics that favour its adoption. Nevertheless, Python still lacks a solution for easily parallelizing generic scripts on distributed infrastructures, since the current alternatives mostly require the use of APIs for message passing or are restricted to embarrassingly parallel computations. In that sense, this paper presents PyCOMPSs, a framework that facilitates the development of parallel computational workflows in Python. In this approach, the user programs her script in a sequential fashion and decorates the functions to be run as asynchronous parallel tasks. A runtime system is in charge of exploiting the inherent concurrency of the script, detecting the data dependencies between tasks and spawning them to the available resources. Furthermore, we show how this programming model can be built on top of a Big Data storage architecture, where the data stored in the backend is abstracted and accessed from the application in the form of persistent objects.

AuRUS: explaining the validation of UML/OCL conceptual schemas

The validation and the verification of conceptual schemas have attracted a lot of interest during the last years, and several tools have been developed to automate this process as much as possible. This is achieved, in general, by assessing whether the schema satisfies different kinds of desirable properties which ensure that the schema is correct. In this paper we describe AuRUS, a tool we have developed to analyze UML/OCL conceptual schemas and to explain their (in)correctness. When a property is satisfied, AuRUS provides a sample instantiation of the schema showing a particular situation where the property holds. When it is not, AuRUS provides an explanation for such unsatisfiability, i.e., a set of integrity constraints which is in contradiction with the property.

Big Data Benchmark Compendium

The field of Big Data and related technologies is rapidly evolving. Consequently, many benchmarks are emerging, driven by academia and industry alike. As these benchmarks are emphasizing different aspects of Big Data and, in many cases, covering different technical platforms and uses cases, it is extremely difficult to keep up with the pace of benchmark creation. Also with the combinations of large volumes of data, heterogeneous data formats and the changing processing velocity, it becomes complex to specify an architecture which best suits all application requirements. This makes the investigation and standardization of such systems very difficult. Therefore, the traditional way of specifying a standardized benchmark with pre-defined workloads, which have been in use for years in the transaction and analytical processing systems, is not trivial to employ for Big Data systems. This document provides a summary of existing benchmarks and those that are in development, gives a side-by-side comparison of their characteristics and discusses their pros and cons. The goal is to understand the current state in Big Data benchmarking and guide practitioners in their approaches and use cases.

Automated reasoning on UML conceptual schemas with derived information and queries

Abstract Context It is critical to ensure the quality of a software system in the initial stages of development, and several approaches have been proposed to ensure that a conceptual schema correctly describes the user’s requirements. Objective The main goal of this paper is to perform automated reasoning on UML schemas containing arbitrary constraints, derived roles, derived attributes and queries, all of which must be specified by OCL expressions. Method The UML/OCL schema is encoded in a first order logic formalisation, and an existing reasoning procedure is used to check whether the schema satisfies a set of desirable properties. Due to the undecidability of reasoning in highly expressive schemas, such as those considered here, we also provide a set of conditions that, if satisfied by the schema, ensure that all properties can be checked in a finite period of time. Results This paper extends our previous work on reasoning on UML conceptual schemas with OCL constraints by considering derived attributes and roles that can participate in the definition of other constraints, queries and derivation rules. Queries formalised in OCL can also be validated to check their satisfiability and to detect possible equivalences between them. We also provide a set of conditions that ensure finite reasoning when they are satisfied by the schema under consideration. Conclusion This approach improves upon previous work by allowing automated reasoning for more expressive UML/OCL conceptual schemas than those considered so far.

Dataclay: A distributed data store for effective inter-player data sharing

Abstract In the Big Data era, both the academic community and industry agree that a crucial point to obtain the maximum benefits from the explosive data growth is integrating information from different sources, and also combining methodologies to analyze and process it . For this reason, sharing data so that third parties can build new applications or services based on it is nowadays a trend . Although most data sharing initiatives are based on public data, the ability to reuse data generated by private companies is starting to gain importance as some of them (such as Google, Twitter, BBC or New York Times) are providing access to part of their data. However, current solutions for sharing data with third parties are not fully convenient to either or both data owners and data consumers. Therefore we present dataClay , a distributed data store designed to share data with external players in a secure and flexible way based on the concepts of identity and encapsulation. We also prove that dataClay is comparable in terms of performance with trendy NoSQL technologies while providing extra functionality, and resolves impedance mismatch issues based on the Object Oriented paradigm for data representation.

Specifying Artifact-Centric Business Process Models in UML

In recent years, the artifact-centric approach to process modeling has attracted a lot of attention. One of the research lines in this area is finding a suitable way to represent the dimensions in this approach. Bearing this in mind, this paper proposes a way to specify artifact-centric business process models by means of well-known UML diagrams, from a high-level of abstraction and with a technology-independent perspective. UML is a graphical language, widely used and with a precise semantics.

mF2C: towards a coordinated management of the IoT-fog-cloud continuum

Fog computing enables location dependent resource allocation and low latency services, while fostering novel market and business opportunities in the cloud sector. Aligned to this trend, we refer to Fog-to-cloud (F2C) computing system as a new pool of resources, set into a layered and hierarchical model, intended to ease the entire fog and cloud resources management and coordination. The H2020 project mF2C aims at designing, developing and testing a first attempt for a real F2C architecture. This document outlines the architecture and main functionalities of the management framework designed in the mF2C project to coordinate the execution of services in the envisioned set of heterogeneous and distributed resources.

Machine Learning-based Query Augmentation for SPARQL Endpoints.

Linked Data repositories have become a popular source of publicly-available data. Users accessing this data through SPARQL endpoints usually launch several restrictive yet similar consecutive queries, either to find the information they need through trial-and-error or to query related resources. However, instead of executing each individual query separately, query augmentation aims at modifying the incoming queries to retrieve more data that is potentially relevant to subsequent requests. In this paper, we propose a novel approach to query augmentation for SPARQL endpoints based on machine learning. Our approach separates the structure of the query from its contents and measures two types of similarity, which are then used to predict the structure and contents of the augmented query. We test the approach on the real-world query logs of the Spanish and English DBpedia and show that our approach yields high-accuracy prediction. We also show that, by caching the results of the predicted (More)

D8.3: Report on Design Model and Definition of Data Directives

Based on the work described in D8.1 and D8.2, we developed initial prototypes for modeling Data Life Cycles and directives. In this document, we are describing both the processes and the results of this initial implementation. We will discuss the issues we faced in developing these prototypes and the technical choices we made. This deliverable provides an overview of the current status of the work. This work has been used as a basis for concrete implementations of community use-cases, described in D8.6.