Context-Aware Performance Benchmarking of a Fleet of Industrial Assets

Industrial assets are instrumented with sensors, connected and continuously monitored. The collected data, generally in form of time-series, is used for corrective and preventive maintenance. More advanced exploitation of this data for very diverse purposes, e.g. identifying underperformance, operational optimization or predictive maintenance, is currently an active area of research. The general methods used to analyze the time-series lead to models that are either too simple to be used in complex operational contexts or too difficult to be generalized to the whole fleet due to their asset-specific nature. Therefore, we have conceived an alternative methodology allowing to better characterize the operational context of an asset and quantify the impact on its performance. The proposed methodology allows to benchmark and profile fleet assets in a context-aware fashion, is applicable in multiple domains (even without ground truth). The methodology is evaluated on real-world data coming from a fleet of wind turbines and compared to the standard approach used in the domain. We also illustrate how the asset performance (in terms of energy production) is influenced by the operational context (in terms of environmental conditions). Moreover, we investigate how the same operational context impacts the performance of the different assets in the fleet and how groups of similarly behaving assets can be determined

kLog for hedge detection

status: publishe

A Text Mining Approach as Baseline for QA4MRE’12

Abstract. This paper describes the participation of the KU Leuven DTAI team in the pilot task on machine reading of biomedical texts about the Alzheimer disease, which is part of the 2012 Question Answering for Machine Reading Evaluation campaign (QA4MRE’12). The main objective of our research was to develop a text mining system as a strong baseline for the task. Based on the outcome of this system, we want to investigate which types of questions can be answered based solely on the input text and the question string, and for which ones we need more advanced techniques that also consider the previously acquired background knowledge from the reference document collection. Furthermore this should give us some insights into the system behavior for specific question types and background information for the development of a tailored inference algorithm

HMM with Non-Emitting States for Map Matching

status: Published onlin

Big Data, de revolutie voorbij de hype

status: publishe

Data mining, interactive semantic structuring, and collaboration: a diversity-aware method for sense-making in search

We present the Damilicious method and tool which help users in sense making of the results of their literature searches on the Web: on an individual level, by supporting the construction of semantics of the domain described by their search term, and on the collective level, by encouraging users to explore and selectively reuse other users' semantics. We use a combination of clustering, classification and interactivity to obtain and apply diverse semantics of thematic areas, and to identify diversity between users. This tool can help users take different perspectives on search results and thereby reflect more deeply about resources on the Web and their meaning. In addition, the method can help to develop quantitative measures of diversity; we propose diversity of resource groupings and diversity of users as two examples.status: publishe

Guiding user groupings

Structuring is one of the fundamental activities needed to understand data. Human structuring activity lies behind many of the datasets found on the Internet that contain grouped instances, such as file or email folders, tags and bookmarks, ontologies and linked data. Understanding the dynamics of large-scale structuring activities is a key prerequisite for theories of individual behaviour in collaborative settings as well as for applications such as recommender systems. In particular, a key question is to what extent the "structurer" - be it human or machine - is driven by his/its own prior structures, and to what extent by the structures created by others such as one's communities. In this paper, we propose a methodology for identifying these dynamics. The methodology relies on dynamic conceptual clustering, and it simulates an intellectual structuring process operating over an extended period of time. The development of a grouping of dynamically changing items follows a dynamically changing and collectively determined "guiding grouping". The analysis of a real-life dataset of a platform for literature management suggests that even in such a typical "Web 2.0" environment, users are guided somewhat more by their own previous behaviour than by their peers.status: publishe

User interest prediction for tweets using semantic enrichment with DBpedia

online publicationstatus: publishe

KUL-Eval: A combinatory categorical grammar approach for improving semantic parsing of robot commands using spatial context

status: publishe