Semantic enrichment of GPS trajectories

Semantic annotation of GPS trajectories helps us to recognize the interests of the creator of the GPS trajectories. Automating this trajectory annotation circumvents the requirement of additional user input. To annotate the GPS traces automatically, two types of automated input are required: 1) a collection of possible annotations, and 2) a collection of GPS trajectories to annotate.\ud \ud The first type of input can be a set of points of interest (POIs), activities, weather types, etc. This collection is to be provided by an application developer, and can originate from the web, an external knowledge base, or an existing database, for example.\ud \ud The type of annotation that we are interested in, is annotation with visited locations, in order to create a user profile at a later stage. We have collected POIs by scraping the web, using a self-configuring data harvester. This harvester is based on workflows, enabling us to add or remove certain steps for different goals of harvesting.\ud \ud The result of our harvesting approach consists of a set of 27,384 POIs, origining from the Dutch Yellow Pages \cite{goudengids2012, and contains an address and a geographical point representation for each POI. These point representations cannot be used to overlay the GPS trajectories directly, and therefore need to be converted into a polygon before providing useful input for the annotation process.\ud \ud Several different approaches to this problem can be thought of, including Voronoi diagrams, nearest-neighbors, and geocoding the addresses of the assumed neighbors. For each of the POI footprint size estimation approaches, the output consists of two parts: 1) a polygon representing the estimated parcel, and 2) an uncertainty function based on the distance to the center of the polygon. These approaches are being validated with cadastral data for the region of Enschede, The Netherlands, and the result of the best approach is used as the input for the GPS trajectory enrichment.\ud \ud The other type of input for the enrichment process is GPS trajectories. This data is generally not smooth, containing outliers, and interruptions of the data stream. Analyzing these imperfections however, may provide valuable information on users entering a rural area, or buildings, respectively.\ud \ud Combining the results of the footprint size estimation with the analyzed GPS trajectory then provides us with uncertain annotated GPS traces

Automated semantic trajectory annotation with indoor point-of-interest visits in urban areas

User trajectories contain a wealth of implicit information. The places that people visit, provide us with information about their preferences and needs. Furthermore, it provides us with information about the popularity of places, for example at which time of the year or day these places are frequently visited. The potential for behavioral analysis of trajectories is widely discussed in literature, but all of these methods need a pre-processing step: the geometric trajectory data needs to be transformed into a semantic collection or sequence of visited points-of-interest that is more suitable for data mining. Especially indoor activities in urban areas are challenging to detect from raw trajectory data. In this paper, we propose a new algorithm for the automated detection of visited points-of-interest. This algorithm extracts the actual visited points-of-interest well, both in terms of precision and recall, even for the challenging urban indoor activity detection. We demonstrate the strength of the algorithm by comparing it to three existing and widely used algorithms, using annotated trajectory data, collected through an experiment with students in the city of Hengelo, The Netherlands. Our algorithm, which combines multiple trajectory pre-processing techniques from existing work with several novel ones, shows significant improvements

Spatiotemporal behavior profiling: a treasure hunt case study

Trajectories have been providing us with a wealth of derived information such as traffic conditions and road network updates. This work focuses on deriving user profiles through spatiotemporal analysis of trajectory data to provide insight into the quality of information provided by users. \ud The presented behavior profiling method assesses user participation characteristics in a treasure-hunt type event. Consisting of an analysis and a profiling phase, analysis involves a timeline and a stay-point analysis, as well as a semantic trajectory inspection relating actual and expected paths. The analysis results are then grouped around profiles that can be used to estimate the user performance in the activity.\ud \ud The proposed profiling method is evaluated by means of a student orientation treasure-hunt activity at the University of Twente, The Netherlands. The profiling method is used to predict the students' gaming behavior by means of a simple team type classification, and a feature-based answer type classification

Generic knowledge-based analysis of social media for recommendations

Recommender systems have been around for decades to help people find the best matching item in a pre-defined item set. Knowledge-based recommender systems are used to match users based on information that links the two, but they often focus on a single, specific application, such as movies to watch or music to listen to. In this presentation, we present our Interest-Based Recommender System (IBRS). This knowledge-based recommender system provides recommendations that are generic in three dimensions: IBRS is (1) domain-independent, (2) language-independent, and (3) independent of the used social medium. To match user interests with items, the first are derived from the user's social media profile, enriched with a deeper semantic embedding obtained from the generic knowledge base DBpedia. These interests are used to extract personalized recommendations from a tagged item set from any domain, in any language. We also present the results of a validation of IBRS by a test user group of 44 people using two item sets from separate domains: greeting cards and holiday homes

//Rondje Zilverling: COMMIT/TimeTrails

Het TimeTrails-project3 gaat over data mining in grote hoeveelheden gegevens over gebeurtenissen in ruimte en tijd, d.w.z. met coördinaten en time-stamps. Dergelijke gegevens worden doorgaans vergaard door mensen, sensoren en wetenschappelijke observaties. Gegevensanalyse richt zich vaak op de vier W’s: Wie, Wat, Waar en Wanneer. Een belangrijke kwestie is het kunnen behappen van de grote hoeveelheden gegevens, d.w.z. "big data". Vanuit de UT werken we, d.w.z. de groepen EWI/DB en ITC/GIP, aan twee applicaties:\ud * Het in kaart brengen van de mening van het publiek bij grote infrastructuurproject zoals de aanleg van een nieuw stuk snelweg. Dit doen we met Twitter-analyse en data-visualisatie.\ud • Het vinden van goede vakantiebestemmingen. Hierbij spelen Social media, web harvesting en analyse van GPS-traces een rol

A systematic review of the energy and climate impacts of teleworking

Information and communication technologies (ICTs) increasingly enable employees to work from home and other locations (‘teleworking’). This study explores the extent to which teleworking reduces the need to travel to work and the consequent impacts on economy-wide energy consumption. Methods/Design: The paper provides a systematic review of the current state of knowledge of the energy impacts of teleworking. This includes the energy savings from reduced commuter travel and the indirect impacts on energy consumption associated with changes in non-work travel and home energy consumption. The aim is to identify the conditions under which teleworking leads to a net reduction in economy-wide energy consumption, and the circumstances where benefits may be outweighed by unintended impacts. The paper synthesises the results of 39 empirical studies, identified through a comprehensive search of 9,000 published articles. Review results/Synthesis: Twenty six of the 39 studies suggest that teleworking reduces energy use, and only eight studies suggest that teleworking increases, or has a neutral impact on energy use. However, differences in the methodology, scope and assumptions of the different studies make it difficult to estimate ‘average’ energy savings. The main source of savings is the reduced distance travelled for commuting, potentially with an additional contribution from lower office energy consumption. However, the more rigorous studies that include a wider range of impacts (e.g. non-work travel or home energy use) generally find smaller savings. Discussion: Despite the generally positive verdict on teleworking as an energy-saving practice, there are numerous uncertainties and ambiguities about its actual or potential benefits. These relate to the extent to which teleworking may lead to unpredictable increases in non-work travel and home energy use that may outweigh the gains from reduced work travel. The available evidence suggests that economy-wide energy savings are typically modest, and in many circumstances could be negative or non-existent

The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase 1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age  6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score  652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc = 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N = 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in Asia and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

Geosocial recommender systems

Daily life is full of location-related decisions: where to go on vacation, which house to buy, which job to apply for, etc. These decisions are not only influenced by the characteristics of this holiday home, house, or company, but also by the region it is located in. What is the distance to the beach, the nearest train station, or the schools and kindergartens? In this thesis, that is inspired by the case of an online holiday home broker, we introduce the geosocial recommender system GeoSoRS: a system that supports people in their decision-making process for location-bound objects, such as holiday homes or real estate, using public data only, to keep the threshold for using GeoSoRS to a minimum.\ud Current location-based recommender systems are focused on the recommendation of a single point-of-interest (POI), based on the characteristics of that POI only. In this thesis, we combine this information about a region in its geoprofile: a description of those characteristics of a region that are relevant to the decision to be made. We try to find the match between users and location-bound objects through a user profile and a geoprofile respectively, and look at the shared interests that users have and regions can satisfy.\ud We find this match by answering four main research questions. First of all, we present a software architecture suitable for the combination of web content, social media data, user-generated content (UGC) and several other sources that provide useful information for recommendation selection.\ud In the second, and largest, part of the thesis, we detect which places are visited by which people through a three-step process: (1) POI collection from the web, (2) estimation of their shape and size, called their polygon-of-interest, using public data only, and (3) matching such two-dimensional polygons-of-interest with user trajectories to detect true visits.\ud Thirdly, we find a way to assess the quality of UGC, based on trajectory characteristics of their creators. We introduce a method for human pattern recognition in trajectory data, and show how the outcome of this pattern detection can be used for the creation of UGC quality prediction models.\ud With the fourth and final research question, we focus on knowledge-based recommendations, combining user interests with the interests that can be satisfied at certain locations. We combine social media data, public generic knowledge-bases and tagged item sets to recommend items from multiple domains, among which holiday homes