Legal aspects of information science, data science, and Big Data ∗

2017 ◽  
pp. 1-46
Author(s):  
Alessandro Mantelero ◽  
Giuseppe Vaciago
Keyword(s):  
Big Data ◽  
Data Science ◽  
Information Science ◽  
Legal Aspects ◽  
Science Data

scholarly journals Occam’s Razor for Big Data? On Detecting Quality in Large Unstructured Datasets

2019 ◽  
Vol 9 (15) ◽  
pp. 3065 ◽  
Author(s):  
Dresp-Langley ◽  
Ekseth ◽  
Fesl ◽  
Gohshi ◽  
Kurz ◽  
...  
Keyword(s):  
Big Data ◽  
Data Science ◽  
Computation Time ◽  
Video Data ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Science Data ◽  
Occam’S Razor ◽  
Principle Of Parsimony ◽  
Occam's Razor

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future.

scholarly journals A view from data science

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110401
Author(s):  
Anna Sapienza ◽  
Sune Lehmann
Keyword(s):  
Big Data ◽  
Data Science ◽  
Common Ground ◽  
Science Data ◽  
Social Scientists

For better and worse, our world has been transformed by Big Data. To understand digital traces generated by individuals, we need to design multidisciplinary approaches that combine social and data science. Data and social scientists face the challenge of effectively building upon each other’s approaches to overcome the limitations inherent in each side. Here, we offer a “data science perspective” on the challenges that arise when working to establish this interdisciplinary environment. We discuss how we perceive the differences and commonalities of the questions we ask to understand digital behaviors (including how we answer them), and how our methods may complement each other. Finally, we describe what a path toward common ground between these fields looks like when viewed from data science.

Fundamentals of deep data science

2020 ◽  
Vol 10 (2) ◽  
pp. 1-6
Author(s):  
Naveena M
Keyword(s):  
Big Data ◽  
Data Science ◽  
Information Science ◽  
Basic Information ◽  
Huge Impact

The present study reports the fundamentals of deep data sciences and their emerging roles across the globe. The influence of deep data science plays important roles in information science. The tools and softwares designed using big data science are creating huge impact on the society. Keeping these into consideration, the study reports the beneficial aspect of it along with basic information.

scholarly journals Various Algorithms & Techniques Driving Data Science for Big Data

2020 ◽  
Vol 9 (5) ◽  
pp. 725-727
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Science ◽  
Information Science ◽  
Big Business ◽  
Level Data

In basic terms, Big Data1 – when joined with Data Science2 – permit chiefs to gauge and survey fundamentally more data about the nuances of their organizations, and to utilize the data in settling on progressively keen choices. In early 2010, during the period when the development of Big Data was truly increasing noteworthy notification all through the 3Data Management industry, said that it "is advancing into the key reason for rivalry." It has now developed, information volumes proceed to develop, and now the inquiry is never again if it's another pattern and what influences it will have, yet how to use Big Data in significant manners for the venture. Information Science has been around for any longer than Big Data, yet it wasn't until the development of information volumes arrived at contemporary levels that Data Science has become an essential part of big business level Data Management.

Research on discipline development and discipline difference of intelligence science in China

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shuqing Li ◽  
Li Ding ◽  
Xiaowei Ding ◽  
Huan Hu ◽  
Yu Zhang
Keyword(s):  
International Comparison ◽  
Historical Development ◽  
Data Science ◽  
Information Science ◽  
Science Research ◽  
Library Science ◽  
Continuous Change ◽  
Science Data ◽  
Content Type ◽  
Intelligence Science

PurposeWith the continuous change of research contents and methods of intelligence science, its integration with other disciplines is also deepening. The purpose of this paper is to further explore the interdisciplinary research characteristics of intelligence science in theoretical depth and application value.Design/methodology/approachThis paper summarizes and explores in two aspects. The first is a large number of literature review, mainly combined with the historical characteristics of the development of intelligence science researches in China and international comparison. The second is to refine the discipline construction ideas suitable for the development of contemporary intelligence science.FindingsFrom the perspective of the historical development of discipline relevance, the development characteristics and positioning of intelligence science in China are introduced, with the comparison of many disciplines including information technology, library science, information science, data science, management science and other disciplines. In order to better meet the practical needs of intelligence service in the new era, this paper mainly analyzes the construction method of intelligence science research system and the relocation of intelligence science research content.Originality/valueThis paper summarizes the historical characteristics and international comparison of the development of intelligence science in China. It proposes the development characteristics and orientation of intelligence science in China from the perspective of historical development of discipline relevance. It also proposes the discipline construction ideas suitable for the development of contemporary intelligence science.

scholarly journals Domains and Contexts on Information Science in the Digital Age: Networks—Information—Data—Software

2014 ◽  
Vol 52 (1(103)) ◽  
pp. 19-42 ◽  
Author(s):  
Kazimierz Krzysztofek
Keyword(s):  
Social Transformation ◽  
Data Science ◽  
Information Science ◽  
Human Beings ◽  
Science Data ◽  
Analog To Digital ◽  
Software Studies ◽  
Theory Of Everything ◽  
Human Thinking ◽  
The Impact

  Purpose/thesis: This paper is an attempt to answer the question whether the fact that human expe­rience and practice are manifested largely or fully within digital environment creates more oppor­tunities to integrate information science that researches the afore-mentioned issue and whether it would allow the researchers to develop coherent knowledge concerning nature, society and human beings. The author does not mean the theory of „everything” but something which has been known for, at least, the last two decades as „third culture”. Approach/methods: The method employed in this, largely sociological, paper was the critical and qualitative discourse analysis. The knowledge on new surfacing social forms and the role of informa­tion in this process remains to be created by social imaginaria rather than experience that is relatively scarce as the society still deals with history in the making. As a result, verified information theories are few and discourses, often contradictory, are very common. In this case the description of each phenomenon, if it is to be exhaustive, needs to be set against various discourses. Results and conclusions: The author defines main concepts functionally and semantically related to the way „information” is understood – an important step if one takes into consideration changes occurring in the process of analog-to-digital shift. The previous paradigm has been exhausted or, at least, the language used for the description of information has aged, which is confirmed by the fact that this paradigm currently hosts more questions than answers. The author criticizes existing disco­urses and proposes his own definitions of new phenomena in the information sphere. The issue of language is very significant - new names for the phenomena influence human thinking, and, inevitably, human actions. As a sociologist the author analyzes it from the perspective of social transformation witnessed by the society the impact of which cannot be recognized and understood at the moment. Originality/value: The paper is an attempt at systematizing and integrating information science issues in four areas: network science, information science, data science and software studies. In Polish and foreign literature those areas are often studied but described separately.

scholarly journals Data-driven smart sustainable cities of the future: urban computing and intelligence for strategic, short-term, and joined-up planning

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Simon Elias Bibri
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Data Science ◽  
Smart Cities ◽  
Data Driven ◽  
Sustainable Cities ◽  
Short Term ◽  
Science Data ◽  
Urban Computing ◽  
The Future

AbstractSustainable cities are quintessential complex systems—dynamically changing environments and developed through a multitude of individual and collective decisions from the bottom up to the top down. As such, they are full of contestations, conflicts, and contingencies that are not easily captured, steered, and predicted respectively. In short, they are characterized by wicked problems. Therefore, they are increasingly embracing and leveraging what smart cities have to offer as to big data technologies and their novel applications in a bid to effectively tackle the complexities they inherently embody and to monitor, evaluate, and improve their performance with respect to sustainability—under what has been termed “data-driven smart sustainable cities.” This paper analyzes and discusses the enabling role and innovative potential of urban computing and intelligence in the strategic, short-term, and joined-up planning of data-driven smart sustainable cities of the future. Further, it devises an innovative framework for urban intelligence and planning functions as an advanced form of decision support. This study expands on prior work done to develop a novel model for data-driven smart sustainable cities of the future. I argue that the fast-flowing torrent of urban data, coupled with its analytical power, is of crucial importance to the effective planning and efficient design of this integrated model of urbanism. This is enabled by the kind of data-driven and model-driven decision support systems associated with urban computing and intelligence. The novelty of the proposed framework lies in its essential technological and scientific components and the way in which these are coordinated and integrated given their clear synergies to enable urban intelligence and planning functions. These utilize, integrate, and harness complexity science, urban complexity theories, sustainability science, urban sustainability theories, urban science, data science, and data-intensive science in order to fashion powerful new forms of simulation models and optimization methods. These in turn generate optimal designs and solutions that improve sustainability, efficiency, resilience, equity, and life quality. This study contributes to understanding and highlighting the value of big data in regard to the planning and design of sustainable cities of the future.

scholarly journals The Art of Data Science and Big Data Analytics: Inspecting and Transforming Data

2020 ◽  
Vol 9 (1) ◽  
pp. 45-56
Author(s):  
Akella Subhadra
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Storage ◽  
Data Analytics ◽  
Data Science ◽  
New Technologies ◽  
Big Data Analytics ◽  
Business Strategies ◽  
Science Data ◽  
Using Data

Data Science is associated with new discoveries, the discovery of value from the data. It is a practice of deriving insights and developing business strategies through transformation of data in to useful information. It has been evaluated as a scientific field and research evolution in disciplines like statistics, computing science, intelligence science, and practical transformation in the domains like science, engineering, public sector, business and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation. In this paper we entitled epicycles of analysis, formal modeling, from data analysis to data science, data analytics -A keystone of data science, The Big data is not a single technology but an amalgamation of old and new technologies that assistance companies gain actionable awareness. The big data is vital because it manages, store and manipulates large amount of data at the desirable speed and time. Big data addresses detached requirements, in other words the amalgamate of multiple un-associated datasets, processing of large amounts of amorphous data and harvesting of unseen information in a time-sensitive generation. As businesses struggle to stay up with changing market requirements, some companies are finding creative ways to use Big Data to their growing business needs and increasingly complex problems. As organizations evolve their processes and see the opportunities that Big Data can provide, they struggle to beyond traditional Business Intelligence activities, like using data to populate reports and dashboards, and move toward Data Science- driven projects that plan to answer more open-ended and sophisticated questions. Although some organizations are fortunate to have data scientists, most are not, because there is a growing talent gap that makes finding and hiring data scientists in a timely manner is difficult. This paper, aimed to demonstrate a close view about Data science, big data, including big data concepts like data storage, data processing, and data analysis of these technological developments, we also provide brief description about big data analytics and its characteristics , data structures, data analytics life cycle, emphasizes critical points on these issues.

Issues in security and privacy of big data

2018 ◽  
Vol 7 (12) ◽  
pp. 1
Author(s):  
Shaveta Bhatia
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Approximate Method ◽  
Biomedical Research ◽  
Cyber Security ◽  
Data Science ◽  
Third Party ◽  
Security And Privacy ◽  
Security Threats ◽  
The Third

 The epoch of the big data presents many opportunities for the development in the range of data science, biomedical research cyber security, and cloud computing. Nowadays the big data gained popularity.  It also invites many provocations and upshot in the security and privacy of the big data. There are various type of threats, attacks such as leakage of data, the third party tries to access, viruses and vulnerability that stand against the security of the big data. This paper will discuss about the security threats and their approximate method in the field of biomedical research, cyber security and cloud computing.

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