A Case Study of Success Factors for Data Warehouse Implementation and Adoption in Sales Planning

We present the case of the successful implementation of a data warehouse for support of the sales planning process in an Austrian company. We investigate the factors that contributed to the success of the project. The key findings of this case study are as follows. First, highly-qualified external consultants may compensate insufficient qualification of internal staff. Of particular importance in that case is communication between internal staff and external consultants. Second, user training compensates a lack of (perceived) usability of the software. Resistance of initially overwhelmed users may be overcome through training sessions. Finally, rather than acquire functionality that is not required, companies should ensure customizability of the acquired software, which is often more important than a plethora of features

A personalized and context-aware news offer for mobile devices

For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer

Overview of NTCIR-15 MART

MART (Micro-activity Retrieval Task) was a NTCIR-15 collaborative benchmarking pilot task. The NTCIR-15 MART pilot aimed to motivate the development of irst generation techniques for high-precision micro-activity detection and retrieval, to support the identiication and retrieval of activities that occur over short time-scales such as minutes, rather than the long-duration event segmentation tasks of the past work. Participating researchers developed and benchmarked approaches to retrieve micro-activities from rich time-aligned multi-modal sensor data. Groups were ranked in decreasing order of micro-activity retrieval accuracy using mAP (mean Average Precision). The dataset used for the task consisted of a detailed lifelog of activities gathered using a controlled protocol of real-world activities (e.g. using a computer, eating, daydreaming, etc). The data included a lifelog camera data stream, biosignal activity (EOG, HR), and computer interactions (mouse movements, screenshots, etc). This task presented a novel set of challenging micro-activity based topics

Overview of the NTCIR-14 Lifelog-3 task

Lifelog-3 was the third instance of the lifelog task at NTCIR. At NTCIR-14, the Lifelog-3 task explored three different lifelog data access related challenges, the search challenge, the annotation challenge and the insights challenge. In this paper we review the activities of participating teams who took part in the challenges and we suggest next steps for the community

UMAP 2018 HUM (Holistic User Modeling) Workshop Chairs’ Preface & Organization

It is our great pleasure to welcome you to the UMAP 2018 HUM (Holistic User Modeling) Workshop. According to a recent claim by IBM, 90% of the data available today have been created in the last two years. This exponential growth of online information has given new life to research in the area of user modeling and personalization, since information about users' preferences, sentiment and opinions, as well as signals describing their physical and psychological state, can now be obtained by mining data gathered from many heterogeneous sources. We can distinguish two important classes of such data sources. One of these comes from recent trends in Quantified Self (QS) and Personal Informatics, which has emphasized the use of technology to collect personal data on different aspects of people's daily lives. These data can be internal states (such as mood or glucose level) or indicators of performance (such as the kilometers run). The purpose of collecting these data is self-monitoring, performed to gain self-knowledge or to obtain some change or improvement (behavioral, psychological, therapeutic, etc.). Often these data are also exploited for behavior change purposes, for example to increase the user's physical activity. The other key category comes from the enormous amount of textual content that is continuously spread on social networks. This has driven a strong research effort to investigate to what extent such data can be exploited to infer user interests, personality traits, emotions, and knowledge. Moreover, the recent phenomenon of (Linked) Open Data fueled this research line by making available a huge amount of machine-readable textual data that can be used to connect all the data points spread in different data silos under a uniform representation formalism. The main goal of the workshop is to investigate whether techniques for advanced content representation and methodologies for gathering and modeling personal data (e.g. physiological, behavioral) can be exploited to build a new generation of personalized and intelligent systems in domains as diverse as health, learning, behavior change, e-government, smart cities (e.g., by combining mood data and music preferences data to provide recommendations on music to be listened)

K-Space at TRECVID 2008

In this paper we describe K-Space’s participation in TRECVid 2008 in the interactive search task. For 2008 the K-Space group performed one of the largest interactive video information retrieval experiments conducted in a laboratory setting. We had three institutions participating in a multi-site multi-system experiment. In total 36 users participated, 12 each from Dublin City University (DCU, Ireland), University of Glasgow (GU, Scotland) and Centrum Wiskunde and Informatica (CWI, the Netherlands). Three user interfaces were developed, two from DCU which were also used in 2007 as well as an interface from GU. All interfaces leveraged the same search service. Using a latin squares arrangement, each user conducted 12 topics, leading in total to 6 runs per site, 18 in total. We officially submitted for evaluation 3 of these runs to NIST with an additional expert run using a 4th system. Our submitted runs performed around the median. In this paper we will present an overview of the search system utilized, the experimental setup and a preliminary analysis of our results