Abstract
The blooming of different cloud data management infrastructures, specialized for different kinds of data and tasks, has led to a wide diversification of DBMS interfaces and the loss of a common programming paradigm. In this paper, we present the design of a cloud multidatastore query language (CloudMdsQL), and its query engine. CloudMdsQL is a functional SQL-like language, capable of querying multiple heterogeneous data stores (relational and NoSQL) within a single query that may contain embedded invocations to each data store's native query interface. The query engine has a fully distributed architecture, which provides important opportunities for optimization. The major innovation is that a CloudMdsQL query can exploit the full power of local data stores, by simply allowing some local data store native queries (e.g. a breadth-first search query against a graph database) to be called as functions, and at the same time be optimized, e.g. by pushing down select predicates, using bind join, performing join ordering, or planning intermediate data shipping. Our experimental validation, with three data stores (graph, document and relational) and representative queries, shows that CloudMdsQL satisfies the five important requirements for a cloud multidatastore query language.

Abstract
Energy consumption is a major problem in Wireless Sensor Networks (WSNs) since in most scenarios nodes operate on batteries with reduced size and low capacity. Battery lifetime estimation is therefore crucial to evaluate the network behavior over time. In addition, battery lifetime estimation is a major challenge since it depends on many factors, such as the duty cycle of supported applications and the intrinsic chemical reactions within the batteries. Several battery models have been proposed to deal with battery lifetime estimation. However, most of the available models address only high-capacity batteries. This paper discusses a method for extracting the parameters for the Kinetic Battery Model (KiBaM) used within WSN context. The target is to define a battery model that helps to estimate the lifetime of nodes using low-capacity batteries and which operate in a duty cycle scheme. Results show that KiBaM is an adequate model for WSNs since it presents low error when compared with experimental results using real batteries.

Abstract
Many real-world data-mining applications involve obtaining predictive models using datasets with strongly imbalanced distributions of the target variable. Frequently, the least-common values of this target variable are associated with events that are highly relevant for end users (e.g., fraud detection, unusual returns on stock markets, anticipation of catastrophes, etc.). Moreover, the events may have different costs and benefits, which, when associated with the rarity of some of them on the available training data, creates serious problems to predictive modeling techniques. This article presents a survey of existing techniques for handling these important applications of predictive analytics. Although most of the existing work addresses classification tasks (nominal target variables), we also describe methods designed to handle similar problems within regression tasks (numeric target variables). In this survey, we discuss the main challenges raised by imbalanced domains, propose a definition of the problem, describe the main approaches to these tasks, propose a taxonomy of the methods, summarize the conclusions of existing comparative studies as well as some theoretical analyses of some methods, and refer to some related problems within predictive modeling.

Abstract
The electrification of the transportation sector is likely to contribute reducing the global dependency on oil and is expected to drive investments to renewable and intermittent energy sources, by taking advantage of it energy storage capacity. In order to facilitate the EV integration to the grid, and to take advantage of the battery storage and the Vehicle-to-Grid (V2G) scheme, smart charging strategies will be required. However, these strategies rarely consider all relevant costs, such as battery degradation. This work analyses the profitability of bidirectional energy transfer, i.e. the possibility of using aggregated EV batteries as storage for energy which can be injected back to the grid, by considering battery degradation as a cost included in the proposed strategy. A mixed integer linear problem (MILP) for minimizing energy costs and battery ageing costs for EV owners is formulated. The battery degradation due to charging and discharging in the V2G scheme is accounted for in the model used. Two case studies of overnight charging of EVs in Sweden and in Spain are proposed. Results show that given current energy prices and battery costs, V2G is not profitable for EV owners, but if battery prices decrease as expected, the V2G will be present in the medium term. © 2016 IEEE.

Abstract
Recommender systems arose in response to the excess of available online information. These systems assign, to a given individual, suggestions of items that may be relevant. These system's monitoring and evaluation are fundamental to the proper functioning of many business related services. It is the goal of this paper to create a tool capable of collecting, aggregating and supervising the results obtained from the recommendation systems' evaluation. To achieve this goal, a multi-granularity approach is developed and implemented in order to organize the different levels of the problem. This tool also aims to tackle the lack of mechanisms to enable visually assessment of the performance of a recommender systems' algorithm. A functional prototype of the application is presented, with the purpose of validating the solution's concept.

Abstract