An Innovative Framework for Effectively and Efficiently Supporting Big Data Analytics over Geo-Located Mobile Social Media

Cuzzocrea, Alfredo; Psaila, Giuseppe; Toccu, Maurizio

doi:10.1145/2938503.2938517

Cited by 17 publications

(9 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first step towards the idea of a loosely-coupled and platform-independent framework for managing JSON data sets was suggested after the work on the FollowMe project [11,12]: in that project, we had the idea of exploiting social media to trace movements of users. We chose the name "FollowMe" because we traced travelers that posted geo-located messages on Twitter; travelers were detected in a pool of 30 airports connected with the airport of Bergamo (northern Italy), since we wanted to study the relevance of this airport in relation to EXPO 2015, held in Milan (only 40 km separate Milan from Bergamo).…”

Section: Background Of the Proposalmentioning

confidence: 99%

J-CO: A Platform-Independent Framework for Managing Geo-Referenced JSON Data Sets

Psaila

Fosci

2021

Electronics

Self Cite

View full text Add to dashboard Cite

Internet technology and mobile technology have enabled producing and diffusing massive data sets concerning almost every aspect of day-by-day life. Remarkable examples are social media and apps for volunteered information production, as well as Open Data portals on which public administrations publish authoritative and (often) geo-referenced data sets. In this context, JSON has become the most popular standard for representing and exchanging possibly geo-referenced data sets over the Internet.Analysts, wishing to manage, integrate and cross-analyze such data sets, need a framework that allows them to access possibly remote storage systems for JSON data sets, to retrieve and query data sets by means of a unique query language (independent of the specific storage technology), by exploiting possibly-remote computational resources (such as cloud servers), comfortably working on their PC in their office, more or less unaware of real location of resources. In this paper, we present the current state of the J-CO Framework, a platform-independent and analyst-oriented software framework to manipulate and cross-analyze possibly geo-tagged JSON data sets. The paper presents the general approach behind the J-CO Framework, by illustrating the query language by means of a simple, yet non-trivial, example of geographical cross-analysis. The paper also presents the novel features introduced by the re-engineered version of the execution engine and the most recent components, i.e., the storage service for large single JSON documents and the user interface that allows analysts to comfortably share data sets and computational resources with other analysts possibly working in different places of the Earth globe. Finally, the paper reports the results of an experimental campaign, which show that the execution engine actually performs in a more than satisfactory way, proving that our framework can be actually used by analysts to process JSON data sets.

show abstract

Section: Background Of the Proposalmentioning

confidence: 99%

J-CO: A Platform-Independent Framework for Managing Geo-Referenced JSON Data Sets

Psaila

Fosci

2021

Electronics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Geo-tagged tweets possibly posted by tourists were gathered through the FollowMe suite [45,46]. It is a software tool that we developed to gather data of moving people from Twitter.…”

Section: Research Project and Case Studymentioning

confidence: 99%

From Data to Rhizomes: Applying a Geographical Concept to Understand the Mobility of Tourists from Geo-Located Tweets

2020

Self Cite

View full text Add to dashboard Cite

In geography, the concept of “rhizome” provides a theoretical tool to conceive the way people move in space in terms of “mobility networks”: the space lived by people is delimited and characterized on the basis of both the places they visited and the sequences of their transfers from place to place. Researchers are now wondering whether in the new era of data-driven geography it is possible to give a concrete shape to the concept of rhizome, by analyzing big data describing movement of people traced through social media. This paper is a first attempt to give a concrete shape to the concept of rhizome, by interpreting it as a problem of “itemset mining”, which is a well-known data mining technique. This technique was originally developed for market-basket analysis. We studied how the application of this technique, if supported by adequate visualization strategies, can provide geographers with a concrete shape for rhizomes, suitable for further studies. To validate the ideas, we chose the case study of tourists visiting a city: the rhizome can be conceived as the set of places visited by many tourists, and the common transfers made by tourists in the area of the city. Itemsets extracted from a real-life data set were used to study the effectiveness of both a topographic representation and a topological representation to visualize rhizomes. In this paper, we study how three different interpretations are actually able to give a concrete and visual shape to the concept of rhizome. The results that we present and discuss in this paper open further investigations on the problem.

show abstract

“…and introduced cloudbased mobile data analytics framework with application case study for smart home based monitoring system. Cuzzocrea et al (2016) have presented innovative FollowMe suite which implements data mining framework for mobile social media analytics with several tools with respective architecture and functionalities. Interesting paper was presented by Torres et al (2017) who addressed data mining methodology and its implementation for congestion prediction in mobile LTE networks tackling also feedback reaction with network reconfigurations trigger.…”

Section: Purpose 4 -To Incorporate Context-awareness Aspectsmentioning

confidence: 99%

Peer Review #1 of "Adaptations of data mining methodologies: a systematic literature review (v0.1)"

2020

View full text Add to dashboard Cite

The use of end-to-end data mining methodologies such as CRISP-DM, KDD process, and SEMMA, has grown substantially over the past decade. However, little is known as to how these methodologies are used in practice. In particular, the question of whether data mining methodologies are used 'as-is' or adapted for specific purposes, has not been thoroughly investigated. This paper addresses this gap via a systematic literature review focused on the context in which data mining methodologies are used and the adaptations they undergo. The literature review covers 207 peer-reviewed and 'grey' publications. We find that data mining methodologies are primarily applied 'as-is'. At the same time, we also identify various adaptations of data mining methodologies and we note that their number is growing rapidly. The dominant adaptations pattern is related to methodology adjustments at a granular level (modifications) followed by extensions of existing methodologies with additional elements. Further, we identify two recurrent purposes for adaptation: (1) adaptations to handle Big Data technologies, tools and environments (technological adaptations); and (2) adaptations for context-awareness and for integrating data mining solutions into business processes and IT systems (organizational adaptations). The study suggests that standard data mining methodologies do not pay sufficient attention to deployment issues, which play a prominent role when turning data mining models into software products that are integrated into the IT architectures and business processes of organizations. We conclude that refinements of existing methodologies aimed at combining data, technological, and organizational aspects, could help to mitigate these gaps.

show abstract

An Innovative Framework for Effectively and Efficiently Supporting Big Data Analytics over Geo-Located Mobile Social Media

Cited by 17 publications

References 24 publications

J-CO: A Platform-Independent Framework for Managing Geo-Referenced JSON Data Sets

J-CO: A Platform-Independent Framework for Managing Geo-Referenced JSON Data Sets

From Data to Rhizomes: Applying a Geographical Concept to Understand the Mobility of Tourists from Geo-Located Tweets

Peer Review #1 of "Adaptations of data mining methodologies: a systematic literature review (v0.1)"

Contact Info

Product

Resources

About