Visual Analytics Supporting Knowledge Management: A Case Study of Germany’s Federal Employment Agency | LSWI
10/2017 Proceedings

Sultanow, Eldar | Tobolla, Marinho | Ullrich, André | Vladova, Gergana

Visual Analytics Supporting Knowledge Management: A Case Study of Germany’s Federal Employment Agency

Abstract

The Federal Employment Agency developed over seven years (and still develops) a mission-critical software system that is responsible for hundred-thousand transactions each day and ensures the safe processing of more than EUR 25 billion per year. This system comprises more than 718.000 lines of code and the development team consists of approx. 90 developers. The main problem in this project is that isolated and highly specialized knowledge (implementation details of Book II of the Social Code) is being developed and not sufficiently shared. For this reason, a new knowledge management system (KMS) that focuses on visualization of knowledge and that includes early warning components has been piloted. This pilot relies on Big Data visualization and visual analytics, pattern recognition (endangered code in terms of knowledge management (KM), knowledge isolation, threat of knowledge loss, lost/orphaned knowledge). KM requirements of a large authority are exceptionally high: external expertise is volatility involved and thus knowledge flows are not very stable. The pilot is intended to use computational clusters and cloud technology and is capable of analyzing and visualizing the change (volume, flow and usage) of knowledge over large periods. This paper describes the motivation, challenges, specifics, and implementation of this KMS pilot system.

Kategorie Proceedings
Autoren Sultanow, Eldar; Tobolla, Marinho; Ullrich, André; Vladova, Gergana
Bandtitel i-KNOW- Data-Driven Future Conference
Datum 10/2017
DOI http://ceur-ws.org/Vol-2025/paper_hci_1.pdf
Keywords Knowledge Management, Public Sector, Mission-critical knowledge, Big Data Visualization, D3.JS, Angular 4, Neo4J