Parallel and Distributed Data Management. Introduction

The manipulation and handling of an ever increasing volume of data by current data-intensive applications require novel techniques for e?cient data management. Despite recent advances in every aspect of data management (storage, access, querying, analysis, mining), future applications are expected to scale to even higher degrees, not only in terms of volumes of data handled but also in terms of users and resources, often making use of multiple, pre-existing autonomous, distributed or heterogeneous resources.

Replication Data Management,needs and solutions: an initial evaluation of conceptual approaches for integrating heterogeneous replication study data

Replication Data Management (RDM) aims at enabling the use of data collections from several iterations of an experiment. However, there are several major challenges to RDM from integrating data models and data from empirical study infrastructures that were not designed to cooperate, e.g., data model variation of local data sources. [Objective] In this paper we analyze RDM needs and evaluate conceptual RDM approaches to support replication researchers. [Method] We adapted the ATAM evaluation process to (a) analyze RDM use cases and needs of empirical replication study research groups and (b) compare three conceptual approaches to address these RDM needs: central data repositories with a fixed data model, heterogeneous local repositories, and an empirical ecosystem. [Results] While the central and local approaches have major issues that are hard to resolve in practice, the empirical ecosystem allows bridging current gaps in RDM from heterogeneous data sources. [Conclusions] The empirical ecosystem approach should be explored in diverse empirical environments.

Interactive Atlas of habitability. Urban data management for the improvement of habitability and coexistence

In this paper we approximate to the understanding of the hybrid city as a context of changes, produced in the perception and in the modes of inhabiting and coexisting in cities through new technologies of information and communication.

Distributed Data Management in 2020?

Work on distributed data management commenced shortly after the introduction of the relational model in the mid-1970's. 1970's and 1980's were very active periods for the development of distributed relational database technology, and claims were made that in the following ten years centralized databases will be an “antique curiosity” and most organizations will move toward distributed database managers [1]. That prediction has certainly become true, and all commercial DBMSs today are distributed.

Meter data management for smart monitoring power networks

The electrical power distribution and commercialization scenario is evolving worldwide, and electricity companies, faced with the challenge of new information requirements, are demanding IT solutions to deal with the smart monitoring of power networks. Two main challenges arise from data management and smart monitoring of power networks: real-time data acquisition and big data processing over short time periods. We present a solution in the form of a system architecture that conveys real time issues and has the capacity for big data management.

Impact of fire and post-fire management techniques on soil chemical properties

The effects of fire ( Control burned soil) and two emergency stabilisation techniques (grass Seeding and straw Mulching ) on 20 chemical characteristics were evaluated on 0 – 5 cm top-soils sampled 1, 90, 180 and 365 days after an experimental fi re in a steep shrubland of a temperate-humid region (NW Spain). Most part of pH (in H 2 O and KCl) variance was explained by the sampling date. No clear temporal trends were identi fi able for total soil C and N content, likely due to the large SOM pool in these soils; however, changes on soil δ 13 C were explained by the deposition of 13 C-depleted ashes, followed by its progressive erosion, while those on soil δ 15 N were a consequence of fi re induced N outputs. After the fi re, NH 4 + – N, P, Na, K, Mg, Ca, Mn, Cu, Zn and B concentrations increased, while those of NO 3 − – N, Al, Fe and Co did not vary significantly. Despite a significant decline with time, concentrations of Mg, Ca and Mn at the end of the study were still higher than in unburned soil, while those of K, Cu, Zn and B were similar to the pre-fire levels and those of NH 4 + – N, P and Na were below pre-fire values. Mulching and Seeding treatments for burned soil emergency stabilisation had significant effects on soil δ 15 N and extractable K, Mg and Ca, while data were inconclusive for their possible effects on the extractable Al, Fe and Co

Language resources and linked data: a practical perspective

Recently, experts and practitioners in language resources have started recognizing the benefits of the linked data (LD) paradigm for the representation and exploitation of linguistic data on the Web. The adoption of the LD principles is leading to an emerging ecosystem of multilingual open resources that conform to the Linguistic Linked Open Data Cloud, in which datasets of linguistic data are interconnected and represented following common vocabularies, which facilitates linguistic information discovery, integration and access. In order to contribute to this initiative, this paper summarizes several key aspects of the representation of linguistic information as linked data from a practical perspective. The main goal of this document is to provide the basic ideas and tools for migrating language resources (lexicons, corpora, etc.) as LD on the Web and to develop some useful NLP tasks with them (e.g., word sense disambiguation). Such material was the basis of a tutorial imparted at the EKAW’14 conference, which is also reported in the paper.