Application of provenance in social computing: A case study

Abstract

Summary Complex systems such as Collective Adaptive Systems that include a variety of resources, are increasingly being designed to include people in task‐execution. Collectives, encapsulating human resources/services, represent one type of an application within which people with different type of skills can be engaged to solve one common problem or work on the same project. Mechanisms of managing social collectives are dependent on functional and non‐functional parameters of members of social collectives. In this work, we investigate the benefits provenance can offer to social computing and trade‐off implications. We show experimental results of how provenance data can help better visualize interaction and performance data during a collective's run‐time. We present novel metrics that can be derived from provenance, and lastly, we discuss privacy implications. If utilized ethically, provenance can help in developing more efficient provisioning and management mechanisms in social computing.

On the Provenance Extraction Techniques from Large Scale Log Files: A Case Study for the Numerical Weather Prediction Models

“…For this purpose, they extend the Open Provenance Model to satisfy the requirements for their case. There exist various other provenance-based systems utilizing the Karma tool [ 21 – 23 ]. In our study, the provenance representation and data model are fully compatible with the W3C consortium’s PROV specification, which defines a common provenance framework that is independent of a specific domain.…”

Association Rule Mining on Big Data Sets

“…In current studies, formulas (using variables such as maximal process length or Vapnik-Chervonenkis (VC) size of the data cluster) considering the data characteristics have appeared [4][5][6][7]. There exists a number of studies focusing on how the management of metadata of big data sets are provided in a distributed computing setting [8][9][10][11]. Moreover, there exists a number of studies that are conducted in the field of information systems for managing distributed data storage platforms [12][13][14][15][16].…”

Semantics, knowledge, and grids at the age of big data and AI

“…Ma et al present an approach to finding influential users of Web events in social media by extracting user behavior network and keyword association network and then using PageRank and HITS to find the influence of users. Riveni et al investigate the benefits that provenance can offer social computing and tradeoff implications. Experimental results of how provenance data can help better visualize interaction and performance data are shown.…”

“…The application class consists of several papers. [10][11][12][13][14] Baeth and Aktas 10 propose an algorithm that assesses information credibility on social networks in order to detect fake or malicious information using a fuzzy analytic hierarchy process to assign proper weights to the proposed metrics. In order to test the usability of the proposed algorithm, it introduces a prototype implementation and tests it on a large-scale synthetic social provenance data set.…”

“…Ma et al 13 present an approach to finding influential users of Web events in social media by extracting user behavior network and keyword association network and then using PageRank and HITS to find the influence of users. Riveni et al 14 investigate the benefits that provenance can offer social…”