844,324 research outputs found
Challenges of Internet of Things and Big Data Integration
The Internet of Things anticipates the conjunction of physical gadgets to the
In-ternet and their access to wireless sensor data which makes it expedient to
restrain the physical world. Big Data convergence has put multifarious new
opportunities ahead of business ventures to get into a new market or enhance
their operations in the current market. considering the existing techniques and
technologies, it is probably safe to say that the best solution is to use big
data tools to provide an analytical solution to the Internet of Things. Based
on the current technology deployment and adoption trends, it is envisioned that
the Internet of Things is the technology of the future, while to-day's
real-world devices can provide real and valuable analytics, and people in the
real world use many IoT devices. Despite all the advertisements that companies
offer in connection with the Internet of Things, you as a liable consumer, have
the right to be suspicious about IoT advertise-ments. The primary question is:
What is the promise of the Internet of things con-cerning reality and what are
the prospects for the future.Comment: Proceedings of the International Conference on International
Conference on Emerging Technologies in Computing 2018 (iCETiC '18), 23rd
-24th August, 2018, at London Metropolitan University, London, UK, Published
by Springer-Verla
Using Ontologies for Semantic Data Integration
While big data analytics is considered as one of the most important paths to competitive advantage of today’s enterprises, data scientists spend a comparatively large amount of time in the data preparation and data integration phase of a big data project. This shows that data integration is still a major challenge in IT applications. Over the past two decades, the idea of using semantics for data integration has become increasingly crucial, and has received much attention in the AI, database, web, and data mining communities. Here, we focus on a specific paradigm for semantic data integration, called Ontology-Based Data Access (OBDA). The goal of this paper is to provide an overview of OBDA, pointing out both the techniques that are at the basis of the paradigm, and the main challenges that remain to be addressed
Integration of survey data and big observational data for finite population inference using mass imputation
Multiple data sources are becoming increasingly available for statistical
analyses in the era of big data. As an important example in finite-population
inference, we consider an imputation approach to combining a probability sample
with big observational data. Unlike the usual imputation for missing data
analysis, we create imputed values for the whole elements in the probability
sample. Such mass imputation is attractive in the context of survey data
integration (Kim and Rao, 2012). We extend mass imputation as a tool for data
integration of survey data and big non-survey data. The mass imputation methods
and their statistical properties are presented. The matching estimator of
Rivers (2007) is also covered as a special case. Variance estimation with
mass-imputed data is discussed. The simulation results demonstrate the proposed
estimators outperform existing competitors in terms of robustness and
efficiency
Task-Driven Big Data Integration
Data integration aims at combining data acquired from different autonomous sources to provide the user with a unified view of this data. One of the main challenges in data integration processes is entity resolution, whose goal is to detect the different representations of the same real-world entity across the sources, in order to produce a unique and consistent representation for it. The advent of big data has challenged traditional data integration paradigms, making the offline batch approach to entity resolution no longer suitable for several scenarios (e.g., when performing data exploration or dealing with datasets that change with a high frequency). Therefore, it becomes of primary importance to produce new solutions capable of operating effectively in such situations.
In this paper, I present some contributions made during the first half of my PhD program, mainly focusing on the design of a framework to perform entity resolution in an on-demand fashion, building on the results achieved by the progressive and query-driven approaches to this task. Moreover, I also briefly describe two projects in which I took part as a member of my research group, touching on some real-world applications of big data integration techniques, to conclude with some ideas on the future directions of my research
Big Data Integration for Data-Centric AI
Big data integration represents one of the main challenges for the use of techniques and tools based on Artificial Intelligence (AI) in several crucial areas: eHealth, energy management, enterprise data, etc. In this context, Data-Centric AI plays a primary role in guaranteeing the quality of the data on which these tools and techniques operate. Thus, the activities of the Database Research Group (DBGroup) of the “Enzo Ferrari” Engineering Department of the University of Modena and Reggio Emilia are moving in this direction. Therefore, we present the main research projects of the DBGroup, which are part of collaborations in various application sectors
- …