Team formation in digital forensics

Staden, Wynand Jc van; Poel, Etienne van der

doi:10.1109/issa.2016.7802934

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…Staden et al also applied Team formation in digital forensics to detect the most suitable group of persons that could have committed a digital crime. This helped reduce the number of suspect groups to start the investigation, resulting in narrowing the search down to the real suspects [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

A novel state space reduction algorithm for team formation in social networks

et al. 2021

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Team formation (TF) in social networks exploits graphs (i.e., vertices = experts and edges = skills) to represent a possible collaboration between the experts. These networks lead us towards building cost-effective research teams irrespective of the geolocation of the experts and the size of the dataset. Previously, large datasets were not closely inspected for the large-scale distributions & relationships among the researchers, resulting in the algorithms failing to scale well on the data. Therefore, this paper presents a novel TF algorithm for expert team formation called SSR-TF based on two metrics; communication cost and graph reduction, that will become a basis for future TF’s. In SSR-TF, communication cost finds the possibility of collaboration between researchers. The graph reduction scales the large data to only appropriate skills and the experts, resulting in real-time extraction of experts for collaboration. This approach is tested on five organic and benchmark datasets, i.e., UMP, DBLP, ACM, IMDB, and Bibsonomy. The SSR-TF algorithm is able to build cost-effective teams with the most appropriate experts–resulting in the formation of more communicative teams with high expertise levels.

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Section: Related Workmentioning

confidence: 99%