Search results for "Big data"

Web mining e Application Programming Interfaces: caratteristiche, strumenti, prospettive e limiti

2014

Models and Streams of Data Journalism

2015

This paper presents the initial results of a two-year research project, the Data Journalism Work Practices, which focuses on newsrooms in Finland, UK and US. Data journalism or data-driven journalism has been defined simply as journalism based on large data sets (or big data) Rogers 2011; Bounegru et al. 2012). According to our ongoing research on data journalism work methods, we can claim this has been an oversimplification. In this paper we will argue that all the brief definitions of data journalism lack nuances, and the multiple layers that contemporary data journalism already consists of in newsrooms. Based on six interviews of leading Finnish, American and British data journalists we …

The Impact of Smart City Initiatives on Human Rights : A Qualitative Research Study

2019

Master's thesis Information Systems IS501 - University of Agder 2019 Context:A smart city is a concept where municipalities use ICTto increase efficiency, sustainability and quality of life forits citizensand city operations. Smart city initiatives useIoT technologies, enabled by ICTs to gather large volumesof unstructured data,known as BigData.The aim of any smart city initiativeisto increase thequality of lifein acity, but posesa threat tocitizens privacy, security andfreedom of expressiondue to the large volumes of databeing collected in smart cities,potentially violatingfundamental rightsin theUniversal Declaration of Human Rights.Purpose:This thesis investigatesthe connection between s…

Mining Maximal Frequent Patterns in Transactional Databases and Dynamic Data Streams: A Spark-based Approach

2018

Mining maximal frequent patterns (MFPs) in transactional databases (TDBs) and dynamic data streams (DDSs) is substantially important for business intelligence. MFPs, as the smallest set of patterns, help to reveal customers’ purchase rules and market basket analysis (MBA). Although, numerous studies have been carried out in this area, most of them extend the main-memory based Apriori or FP-growth algorithms. Therefore, these approaches are not only unscalable but also lack parallelism. Consequently, ever increasing big data sources requirements cannot be met. In addition, mining performance in some existing approaches degrade drastically due to the presence of null transactions. We, therefo…

Emerging Trends, Issues, and Challenges in Big Data and Its Implementation toward Future Smart Cities

2017

The articles in this special section focus on Big Data as it impacts future smart cities. The world is experiencing a period of extreme urbanization. Moreover, this process will continue, and the global urban population is expected to double by 2050. Smart city has been proposed to improve the efficiency of services and meet residents’ needs for better quality of life. Essentially, smart city integrates the Internet of Things and emerging communication technologies such as fifth generation (5G) solutions to manage the citys’ assets, including transportation systems, hospitals, water supply networks, waste management, and so on. Therefore, smart city is driving innovation and new technologie…

Open challenges in environmental data analysis and ecological complex systems (a)

2020

Abstract This letter focuses on open challenges in the fields of environmental data analysis and ecological complex systems. It highlights relations between research problems in stochastic population dynamics, machine learning and big data research, and statistical physics. Recent and current developments in statistical modeling of spatiotemporal data and in population dynamics are briefly reviewed. The presentation emphasizes stochastic fluctuations, including their statistical representation, data-based estimation, prediction, and impact on the physics of the underlying systems. Guided by the common thread of stochasticity, a deeper and improved understanding of environmental processes an…

Et­no­gra­fia val­jas­taa ”ti­heän da­tan” yri­tys­ten käyt­töön

2017

Antti Kiviranta ”Iso data” (big data) on ollut analytiikan ja markkinoinnin muotisana jo kohta 20 vuotta. Tietoyhteiskunta jossa elämme tuottaa ja tallentaa koko ajan käsittämättömiä määriä informaatiota päivittäisestä käyttäytymisestämme. Oikeilla työkaluilla tästä valtavasta tietomassasta voidaan suodattaa ja prosessoida hyvinkin yksityiskohtaisia havaintoja, joiden pohjalta johtamisen, markkinoinnin ja tuotekehityksen toimintastrategioita voidaan tehostaa.

Balanced Large Scale Knowledge Matching Using LSH Forest

2015

Evolving Knowledge Ecosystems were proposed recently to approach the Big Data challenge, following the hypothesis that knowledge evolves in a way similar to biological systems. Therefore, the inner working of the knowledge ecosystem can be spotted from natural evolution. An evolving knowledge ecosystem consists of Knowledge Organisms, which form a representation of the knowledge, and the environment in which they reside. The environment consists of contexts, which are composed of so-called knowledge tokens. These tokens are ontological fragments extracted from information tokens, in turn, which originate from the streams of information flowing into the ecosystem. In this article we investig…

Taming big knowledge evolution

2016

Information and its derived knowledge are not static. Instead, information is changing over time and our understanding of it evolves with our ability and willingness to consume the information. When compared to humans, current computer systems seem very limited in their ability to really understand the meaning of things. On the other hand, they are very powerful when it comes down to performing exact computations. One aspect which sets humans apart from machines when trying to understand the world is that we will often make mistakes, forget information, or choose what to focus on. To put this in another perspective, it seems like humans can behave somehow more randomly and still outperform …

Data obstacles and privacy concerns in artificial intelligence initiatives

2021

To become and remain competitive, many companies (especially large ones) are considering capitalising on data-based technologies, such as Artificial Intelligence (AI). However, whether these companies are structurally ready in terms of data collection and management remains unknown. This chapter discusses how privacy issues and reforms, such as the General Data and Protection Regulation (GDPR), affect companies’ AI initiatives and processes. For this purpose, we reviewed the relevant literature and collected empirical data using in-depth interviews with AI and data industry experts in five countries. Our main findings indicated that companies are lacking sound data collection and management…