In this paper, we present the main challenges “big data” raises to information systems, that is to systems dedicated to the storage and processing of data for decision making purposes. After presenting in detail two major applications of big data (information retrieval and business intelligence), we investigate the role of open data and the web in big bata applications, as well as the role the ...

As the Internet-of-Vehicles (IoV) technology becomes an increasingly important trend for future transportation, designing large-scale IoV systems has become a critical task that aims to process big data uploaded by fleet vehicles and to provide data-driven services. The IoV data, especially high-frequency vehicle statuses (e.g., location, engine parameters), are characterized as large volume wi...

Large datasets introduce challenges to the scalability of query answering. Given a query Q and a dataset D, it is often prohibitively costly to compute the query answers Q(D) when D is big. To this end, one may want to use heuristics, “quick and dirty” algorithms which return approximate answers. However, in many applications it is a must to find exact query answers. So, how can we efficiently ...

The 5 XLDB workshop brought together scientific and industrial users, developers, and researchers of extremely large data and focused on emerging challenges in the healthcare and genomics communities, spreadsheet-based large scale analysis, and challenges in applying statistics to large scale analysis, including machine learning. Major problems discussed were the lack of scalable applications, ...

In the age of big data, the data quality problem is more severe than ever. As an essential step in data cleaning, similarity join has attracted lots of attentions from the database community. In this work, to address the similarity join problem with edit-distance constraints, we first improve the partition-based join algorithm for small scale data. Then we extend the algorithm based on MapReduc...

We contend that existing datasets based on survey or administrative data queried via standard industrial and occupational standards are ill suited for the purposes of innovation policy, and therefore, of its ability to attain its goals to address market, system and emergence failures preventing new ideas from being applied. These datasets are constrained in their ability to identify novel secto...

Reconstruction is a key step of the motion capture process. The quality of motion data first results from the quality of raw data. However, it also depends on the motion reconstruction step, especially when raw data suffer markers losses or noise due, for example, to challenging conditions of capture. Labeling is a final and crucial data reconstruction step that enables practical use of motion ...

The topic of Data Quality (DQ) in the field of Information Management has been extensively researched. Within this field, DQ relevant for Public Health Administration has been relatively less explored. DQ research has traditionally focused on a select set of DQ factors (e.g. timeliness or accuracy etc.), however, advances in the field of Information Management triggered by emerging technology t...

Big data management is no longer an issue for large enterprises only; it has also become a challenge for small and middle-sized enterprises, too. Today, enterprises have to handle business data and processes of increasing complexity that are almost entirely electronic in nature, regardless of enterprises’ size. Enterprises’ information systems need functions based on specific technologies to be...