Technical Perspective

2021 ◽  
Vol 50 (1) ◽  
pp. 5-5
Author(s):  
Graham Cormode
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Streams ◽  
Random Access ◽  
Streaming Algorithms ◽  
Monitoring Networks ◽  
Full Data ◽  
The Past

Over the past two decades the data management community has devoted particular attention to handling data that arrives as a stream of updates. This captures a number of "big data" scenarios, ranging from monitoring networks to processing high volumes of transactions in commerce and finance. This has led to data streams becoming a mainstream data management topic, with many systems offering explicit support for handling such inputs. Within these systems, streaming algorithms are used to approximate various statistical and modeling queries, which would traditionally require random access to the full data to compute exactly.

scholarly journals Big Data Management: What to Keep from the Past to Face Future Challenges?

2017 ◽  
Vol 2 (4) ◽  
pp. 328-345 ◽  
Author(s):  
G. Vargas-Solar ◽  
J. L. Zechinelli-Martini ◽  
J. A. Espinosa-Oviedo
Keyword(s):  
Big Data ◽  
Data Management ◽  
The Past ◽  
Future Challenges

scholarly journals BIG DATA IN CIVIL ENGINEERING: A STATE-OF-THE-ART SURVEY

2016 ◽  
Vol 8 (4) ◽  
pp. 165-175 ◽  
Author(s):  
Oleg Kapliński ◽  
Natalija Košeleva ◽  
Guoda Ropaitė
Keyword(s):  
Big Data ◽  
Data Management ◽  
Core Collection ◽  
Web Of Science ◽  
State Of The Art ◽  
Funding Agency ◽  
Data Generation ◽  
Raw Data ◽  
The Past ◽  
Thomson Reuters

Data generation has increased drastically over the past few years. Data management has also grown in importance because extracting the significant value out of a huge pile of raw data is of prime importance while making different decisions. This article reviews the concept of Big Data. The Thomson Reuters Web of Science Core Collection academic database was used to overview publications that contained “BIG DATA” keywords and were included in Web of Science Category under “Engineering”. The analysis of publications was made according to year, country, journal, authors, language and funding agency.

Little Teams, Big Data: Big Data Provides New Opportunities for Teams Theory

2015 ◽  
Vol 8 (4) ◽  
pp. 550-555 ◽  
Author(s):  
Dorothy R. Carter ◽  
Raquel Asencio ◽  
Amy Wax ◽  
Leslie A. DeChurch ◽  
Noshir S. Contractor
Keyword(s):  
Big Data ◽  
Social Science ◽  
Data Streams ◽  
Theoretical Models ◽  
Social Science Theory ◽  
The Past ◽  
Science Theory ◽  
Value Add ◽  
Scientific Endeavor ◽  
Collective Goals

Over the past 25 years, industrial and organizational (I-O) psychologists have made great strides forward in the area of teams research. They have developed and tested meso-level theories that explain and predict the behavior of individuals in teams and teams operating within and across organizations. The continued contributions of I-O psychologists to theory and research on teams require us to address the challenges—several of which were well described in the focal article (Guzzo, Fink, King, Tonidandel, & Landis, 2015)—and embrace the opportunities that are being ushered in by big and broad data streams (Hendler, 2013). We suggest that a principal unique value add of the I-O psychologist to the basic scientific endeavor of understanding small teams comes in the form of theory—theories that explain why, when, how, and to what end individuals form relationships needed for teams to function in unison toward the accomplishment of collective goals. Some have argued that the big data revolution means “the end of theory,” suggesting petabyte data render theoretical models obsolete (Anderson, 2008). On the contrary, we submit that big-data enabled social science holds the promise of rapid progress in social science theory, particularly in the area of teams.

scholarly journals DaLiF: a data lifecycle framework for data-driven governments

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Syed Iftikhar Hussain Shah ◽  
Vassilios Peristeras ◽  
Ioannis Magnisalis
Keyword(s):  
Big Data ◽  
Data Management ◽  
Good Governance ◽  
Business Community ◽  
Data Driven ◽  
Governmental Organizations ◽  
Data Lifecycle ◽  
The Government ◽  
Data Ecosystem ◽  
Governance Transparency

AbstractThe public sector, private firms, business community, and civil society are generating data that is high in volume, veracity, velocity and comes from a diversity of sources. This kind of data is known as big data. Public Administrations (PAs) pursue big data as “new oil” and implement data-centric policies to transform data into knowledge, to promote good governance, transparency, innovative digital services, and citizens’ engagement in public policy. From the above, the Government Big Data Ecosystem (GBDE) emerges. Managing big data throughout its lifecycle becomes a challenging task for governmental organizations. Despite the vast interest in this ecosystem, appropriate big data management is still a challenge. This study intends to fill the above-mentioned gap by proposing a data lifecycle framework for data-driven governments. Through a Systematic Literature Review, we identified and analysed 76 data lifecycles models to propose a data lifecycle framework for data-driven governments (DaliF). In this way, we contribute to the ongoing discussion around big data management, which attracts researchers’ and practitioners’ interest.

scholarly journals Introduction to the Special Issue on New Longitudinal Data for Retirement Analysis and Policy

2021 ◽  
pp. 1-5
Author(s):  
Marco Angrisani ◽  
Anya Samek ◽  
Arie Kapteyn
Keyword(s):  
Big Data ◽  
Academic Research ◽  
Cost Effective ◽  
Research Data ◽  
Data Sources ◽  
Special Issue ◽  
Administrative Records ◽  
The Past ◽  
Survey Questionnaires ◽  
Internet Panels

The number of data sources available for academic research on retirement economics and policy has increased rapidly in the past two decades. Data quality and comparability across studies have also improved considerably, with survey questionnaires progressively converging towards common ways of eliciting the same measurable concepts. Probability-based Internet panels have become a more accepted and recognized tool to obtain research data, allowing for fast, flexible, and cost-effective data collection compared to more traditional modes such as in-person and phone interviews. In an era of big data, academic research has also increasingly been able to access administrative records (e.g., Kostøl and Mogstad, 2014; Cesarini et al., 2016), private-sector financial records (e.g., Gelman et al., 2014), and administrative data married with surveys (Ameriks et al., 2020), to answer questions that could not be successfully tackled otherwise.

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