Public Administration II

Session 2.2

Wednesday, September 16, 2015 - 14:30 to 16:00
LC Ceremonial hall 2
Tassilo Pellegrini


Director of Information Services


Using Semantics to Enhance Bodyworn Camera Video for Law Enforcement

As law enforcement agencies around the world consider the use of police officer body­worn cameras to capture critical evidence during interactions with the public, what's being overlooked is how to effectively use the massive amount of video data being uploaded every day. This session will cover strategies for integrating semantics into the use of body camera video for investigations, officer training and public awareness.


The MIP Information Model

The Multilateral Interoperability Programme (MIP) is a multinational military standardization committee with participants from 24 member nations and NATO. It develops interoperability specifications for Command and Control Information Systems (C2IS). A key product is the MIP Information Model (MIM) that serves as a standard for information exchange for multiple echelons in joint and combined operations. Technically, the MIM is based on UML, extended by so-called UML profiles that constitute the MIM meta model. The MIM refers to various legacy data models and is under continuous development for enabling interoperability under changing operational requirements. To ensure model soundness and consistency, it comes with a suite of sophisticated tools for semantic analysis and configuration management. It seeks to close the gap between the domain expert on the one hand and the software implementer on the other hand, enabling model-driven software development. To this end, several transformations for the MIM have been defined. Among them is a transformation to OWL2. The derivation of an OWL ontology from the MIM makes it possible to add domain knowledge that cannot be expressed adequately with UML. The OWL-transformation is thus an important step in constructing a commonly agreed upon, extensible C2 ontology.


Using semantic technologies to identify the concerns of a caller in a big contact center of Stadt Wien – Wiener Wohnen

As one of the largest property management companies in Europe Stadt Wien - Wiener Wohnen (WW) manages approximately 220,000 community-owned apartments, 47,000 parking spaces and 5,500 shops. More than half a million tenants, and thus about a quarter of Vienna's city population, cause 1.5 million customer inquiries to the contact center per year. The reported customer issues are manifold, ranging from technical defects, suggestions, information and complaints to commercial issues about rent and operating costs. This large variety of topics and the proper selection of associated procedures for handling the concerns remain for the employees of the contact center a major challenge. In particular taking into account the fact that some by the call center initiated businesses process very high costs.

To increase the quality and speed of the concern identification, WW implemented the cognitive decision system DEEP.assist, which went live in June 2014. With DEEP.assist the call center agent now only has to type in the statements of the caller in form of normal German sentences. Doing this, the call center agent documents the business case and the system analyses additionally in real time the meaning of the text and the call center agent gets proposals for the solution already during the writing. A key challenge in problem solving was the fact, that the caller often does not describe the specific problem, but articulates the symptoms of the concern. With the help of chains of associations DEEP.assist is able to identify the concerns, even with very unusual descriptions of the caller.