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The
Database
Group
(DBGroup, www. dbgroup. unimore. it) and
Information
System
Group
(ISGroup, www. isgroup. unimore. it) research
activities
have
been
mainly
devoted to the Data Integration Reserach Area. The DBGroup designed and developed the MOMIS data integration system, giving raise to a successful innovative enterprise DataRiver (www. datariv...
Citations
... Big Data [3] presents a severe challenge in terms of volume, velocity, variety and veracity (4V of Big Data) and often exhibits intrinsic issues and a sparse, scarce, and unbalanced nature. Numerous approaches are available within the Data Integration literature to address different facets of data quality [4]. However, the incorporation of privacy requirements pose additional challenges and necessitates the modification of traditional process through the adaptation of pre-existing approaches and methodologies and the development of innovative privacy-preserving techniques. ...
... Jose M. Sanchez et.al. [20], described the Optimal Ablation Techniques for Ventricular Tachycardia Management. Wilbur J et.al. ...
... Many healthcare companies provide and integrate these medical technologies into a daily medical routine. For instance, healthcare companies have developed unique "apps" [20], for patients who are unable to go to the clinic for their routine checkups. These apps are directly connected to the clinician's or doctor's user terminal. ...
The International Journal of Machine Learning and Networked Collaborative
Engineering (IJMLNCE) ISSN 2581-3242 continues to evolve and expand, receiving more
and more quality articles for evaluation and possible publication. We are happy to share
with you that apart from the existing indexing, we are able to place our journal manuscript
with two more indexing e.g., WorldCat-OCLC and Dimensions. We are now proud to
present the Volume No-02 Issue No-03, on this occasion, we have selected five interesting
papers that are framed in the scope of the journal, covering different aspects related to
machine learning and collaborative engineering.
Küçük and Kiani [1] published a work entitled “Smart Advisor: An Intelligent
Inventory Prediction Based On Regression Model”. Authors focus on inventory
management of raw material and stock amounts in enterprises and present a model to
predict the demand of stock items by using a regression model. They analyze the outputs
of the model on a sample dataset to enable accurate estimation of the amount of stock to be consumed in the future and to facilitate decision making.
Kalaskar et al. [2] published a work entitled “Forecasting Ventricular Deviation in
Monitoring of Live ECG Signal”. This work shows the problem of the increasing number
of coronary artery diseases and ventricular arrhythmias cases. Authors propose a novel
platform for real time diagnosis of Ventricular Tachyarrhythmia with the help of a portable
electrocardiography device. In addition, it includes a solution for signal analysis and
cloud-based processing for the diagnosis.
Hoang et al. [3] published a work entitled “Cow Behavior Monitoring Using a
Multidimensional Acceleration Sensor and Multiclass SVM”. In this work, authors talk
about the health of cows based on their daily behavior. Thus, they propose an automated
monitoring system for suitable management. Cow’s activities are monitored by using a
multidimensional acceleration sensor and data is processed in a server through an
algorithm based on multiclass support vector machine.
Kumar and Sairam [4] published a work entitled “Machine Learning Approach for
User Accounts Identification with Unwanted Information and data”. Authors focus on
identifying fake and suspicious accounts in Facebook in an effective way through a novel
architecture and a process flow. They also apply machine learning supervised models for
text classification and machine learning unsupervised models for image classification
respectively.
Puri et al [5] published a work entitled “Internet of Things and Healthcare
Technologies: A Valuable Synergy from Design to Implementation”. In this work, authors
introduce a review on various enabling Internet of Medical Things technologies based on
the latest research work and technology available in the marketplace. The work also
analyzes different software platforms available in the field and the current challenges that
the industry is addressing.
REFERENCES
[1] Küçük, Ömer, & Kiani, F. (2018). Smart Advisor: An Intelligent Inventory Prediction
Based On Regression Model. International Journal of Machine Learning and
Networked Collaborative Engineering, 2(03), pp. 86-94.
https://doi.org/10.30991/IJMLNCE.2018v02i03.001
[2]Kalaskar, R., Harsoor, D. B., & Das, R. (2018). Forecasting Ventricular Deviation in
Monitoring of Live ECG Signal. International Journal of Machine Learning and
Networked Collaborative Engineering, 2(03), pp. 95-109.
https://doi.org/10.30991/IJMLNCE.2018v02i03.002
[3]Hoang, Q.-T., Phi Khanh, P. C., TrungNinh, B., Phuong Dung, C. T., & Tran, T.
(2018). Cow Behavior Monitoring Using a Multidimensional Acceleration Sensor and
Multiclass SVM. https://doi.org/10.30991/IJMLNCE.2018v02i03.003
[4]Kumar, A., & SAIRAM, T. (2018). Machine Learning Approach for User Accounts
Identification with Unwanted Information and data. International Journal of Machine
Learning and Networked Collaborative Engineering, 2(03),pp. 119-127.
https://doi.org/10.30991/IJMLNCE.2018v02i03.004
[5] Puri, V., Gautam, K., Troump, J., Le, C., & Nguyen, N. (2018). Internet of Things and
Healthcare Technologies: A Valuable Synergy from Design to Implementation.
International Journal of Machine Learning and Networked Collaborative Engineering,
2(03), pp. 128-142. https://doi.org/10.30991/IJMLNCE.2018v02i03.005
... Making file metadata scalable is one of the top challenges addressed in the literature [49]. In connection to this, scalable data integration methods have been also investigated [26,48]. ...
Big Data technology has discarded traditional data modeling approaches as no longer applicable to distributed data processing. It is, however, largely recognized that Big Data impose novel challenges in data and infrastructure management. Indeed, multiple components and procedures must be coordinated to ensure a high level of data quality and accessibility for the application layers, e.g., data analytics and reporting. In this paper, the third of its kind co-authored by members of IFIP WG 2.6 on Data Semantics, we propose a review of the literature addressing these topics and discuss relevant challenges for future research. Based on our literature review, we argue that methods, principles, and perspectives developed by the Data Semantics community can significantly contribute to address Big Data challenges.
... Bergamaschi et al. [21] contend that keyword search over relational databases is still a considerable challenge, despite the effort that the research community has put in the field in the last fifteen years and the significant number of scientific publications and prototypes developed. The authors discuss two main issues that have hampered the design and development of next generation systems for keyword search over structured data: (i) the lack of systemic approaches that account for all the issues of keyword search, from the interpretation of user needs to the computation, retrieval, ranking and presentation of the results; and (ii) the absence of a shared and complete evaluation methodology measuring user satisfaction, achieved utility and required effort for carrying out informative tasks. ...
The Web is the main channel for circulating information in modern society. Such information can be found in various storage media, therefore an interface is needed to retrieve it. As a simple and effective technique, keyword-based querying has proved ideal for this purpose and has become the interaction standard between users and the Web. However, most of the information found on the Web is currently stored in relational databases, and such repositories offer limited support for keyword-based queries. Performing queries in relational databases requires knowledge of storage structures and syntax of a structured language, and both are not familiar to the majority of users. Given keyword-based queries, this study presents a method that integrates search and selection of relational databases available on the Web, and semantic query processing to retrieve relevant information to the user.
The Energy Community Platform (ECP) is a modular system conceived to promote a conscious use of energy by the users inside local energy communities. It is composed of two integrated subsystems: the Energy Community Data Platform (ECDP), a middleware platform designed to support the collection and the analysis of big data about the energy consumption inside local energy communities, and the Energy Community Tokenization Platform (ECTP), which focuses on tokenizing processed source data to enable incentives through smart contracts hosted on a decentralized infrastructure possibly governed by multiple authorities. We illustrate the overall design of our system, conceived considering some real-world projects (dealing with different types of local energy community, different amounts and nature of incoming data, and different types of users), analyzing in detail the key aspects of the two subsystems. In particular, the ECDP acquires data of different nature in a heterogeneous format from multiple sources and supports a data integration workflow and a data lake workflow, designed for different uses of the data. We motivate our technological choices and present the alternatives taken into account, both in terms of software and of architectural design. On the other hand, the ECTP operates
a tokenization process via smart contracts to promote good behavior of users within the local energy community. The peculiarity of this platform is to allow external parties to audit the correct behavior of the whole tokenization process while protecting the confidentiality of data and the performance of the platform. The main strengths of the presented system are flexibility and scalability (guaranteed by its modular architecture), which allow its applicability to any type of local energy community.
Анализируется развитие системы военно-научной информации, раскрываются особенности и проблемы современного этапа развития информационного обеспечения Минобороны России, предлагается современный подход к организации научно-информационной деятельности органов военного управления и научно-исследовательских организаций Минобороны России.
Keyword-based query specification to extract data from structured databases has attracted considerable attention from various researchers, and many interesting proposals may be found in the scientific literature. However, many of these studies focus on finding a set of interconnected tuples containing all or some of the query keywords. The architecture introduced by this paper covers from the selection of databases on the Web to ranked relevant results. The approach also includes important aspects such as the proximity between keywords, query segmentation, and the use of aggregate functions, among others. The empirical evaluation analyzes the relevance of results and proves competitive as regards related studies.
With the progress of new technologies of information and communication, more and more producers of data exist. On the other hand, the web forms a huge support of all these kinds of data. Unfortunately, existing data is not proper due to the existence of the same information in different sources, as well as erroneous and incomplete data. The aim of data integration systems is to offer to a user a unique interface to query a number of sources. A key challenge of such systems is to deal with conflicting information from the same source or from different sources. We present, in this paper, the resolution of conflict at the instance level into two stages: references reconciliation and data fusion. The reference reconciliation methods seek to decide if two data descriptions are references to the same entity in reality. We define the principles of reconciliation method then we distinguish the methods of reference reconciliation, first on how to use the descriptions of references, then the way to acquire knowledge. We finish this section by discussing some current data reconciliation issues that are the subject of current research. Data fusion in turn, has the objective to merge duplicates into a single representation while resolving conflicts between the data. We define first the conflicts classification, the strategies for dealing with conflicts and the implementing conflict management strategies. We present then, the relational operators and data fusion techniques. Likewise, we finish this section by discussing some current data fusion issues that are the subject of current research.
