Abstract Time series classification(TSC) aims to predict the class label of a given time series, which is critical to a rich set of application areas such as economics and medicine. State-of-the-art TSC methods have mostly focused on classification accuracy, without considering classification speed. However, efficiency is important for big data analysis. Datasets with a large training size or long series challenge the use of the current highly accurate methods, because they are usually computationally expensive. Similarly, classification explainability, which is an important property required by modern big data applications such asappliance modelingand legislation such as theEuropean General Data Protection Regulation, has received little attention. To address these gaps, we propose a novel TSC method – theRandomized-Supervised Time Series Forest(r-STSF). r-STSF is extremely fast and achieves state-of-the-art classification accuracy. It is an efficient interval-based approach that classifies time series according to aggregate values of the discriminatory sub-series (intervals). To achieve state-of-the-art accuracy, r-STSF builds an ensemble of randomized trees using the discriminatory sub-series. It uses four time series representations, nine aggregation functions and a supervised binary-inspired search combined with a feature ranking metric to identify highly discriminatory sub-series. The discriminatory sub-series enable explainable classifications. Experiments on extensive datasets show that r-STSF achieves state-of-the-art accuracy while being orders of magnitude faster than most existing TSC methods and enabling for explanations on the classifier decision.

Assessing OpenStreetMap (OSM) data quality against authoritative data sources may not always be viable. This is primarily because of the multi-dimensional nature and heterogeneity of the maps, yet the activity is pivotal for targeted data cleansing and quality enhancement undertakings in these data sets. A salient facet of OSM, allowing contributors to flag potential problems encountered during the mapping process, is the FIXME tag. In this article, we examine and discuss OSM data quality through the vast expanse of issues (knowledge) documented via FIXME. We present a classification and analysis of these quality issues, exposed as topic models and grounded in the ISO-19157 standard, across USA and Australia. Regional distributions of these topics are further qualitatively analyzed to ascertain the variation of key issues in OSM. We also present a comparison of the intrinsic issue classification against those identified in an issue corpus of an authoritative map data source. Due to the considerable heterogeneity in user mapping and reporting, OSM issue detection and classification remains problematic. This research presents a flexible and intrinsic data-mining approach, linking established ISO data quality standards to OSM issue categorization. Our work, thus informs the development of automated error correction methods for VGI datasets.

The term between is frequently used to describe spatial arrangements of objects where one described core object is positioned in the space bounded by two or more peripheral objects. As such, the relation between involves spatial configurations of at least three spatial objects. However, most of the existing qualitative spatial reasoning models focus only on binary spatial relations, and there is currently no single model that enables adequate reasoning about this ternary spatial relation. This paper proposes a novel model for expressing nuanced spatial relationships between three spatial objects, called the Ray Intersection Model (RIM). RIM evaluates rays cast between two peripheral spatial objects, and their topological relations with the core object to determine its position relative to the peripheral objects. RIM leaves the binary classification of the core object as between/not between to the user and application context. Although RIM supports all types of 2D spatial objects (i.e. points, lines, and polygons), its expressiveness is demonstrated in this paper by analyzing the total of 28 distinct configurations of triplets of polygon objects in a 2D plane. RIM has been computationally implemented and we demonstrate how RIM can be applied to analyze the arrangements of buildings at a university campus.

We present an ongoing collaboration between computer science researchers and fact-checking experts in a broad-cast corporation to develop Watch 'n' Check, a social media monitoring tool that assists fact-checkers to detect and target misinformation online. The lean methodology followed in our collaboration has helped us to better understand how information access tools can assist fact-checking experts. We report initial results and discuss our plan for further development, as well as the open challenges identified so far.

Time series classification (TSC) aims to predict the class label of a given time series. Modern applications such as appliance modelling require to model an abundance of long time series, which makes it difficult to use many state-of-the-art TSC techniques due to their high computational cost and lack of interpretable outputs. To address these challenges, we propose a novel TSC method: the Supervised Time Series Forest (STSF). STSF improves the classification efficiency by examining only a (set of) sub-series of the original time series, and its tree-based structure allows for interpretable outcomes. STSF adapts a top-down approach to search for relevant sub-series in three different time series representations prior to training any tree classifier, where the relevance of a sub-series is measured by feature ranking metrics (i.e., supervision signals). Experiments on extensive real datasets show that STSF achieves comparable accuracy to state-of-the-art TSC methods while being significantly more efficient, enabling TSC for long time series.

The ubiquity of personal sensing devices has enabled the collection of large, diverse, and fine-grained spatio-temporal datasets. These datasets facilitate numerous applications from traffic monitoring and management to location-based services. Recently, there has been an increasing interest in profiling individuals' movements for personalized services based on fine-grained trajectory data. Most approaches identify the most representative paths of a user by analyzing coarse location information, e.g., frequently visited places. However, even for trips that share the same origin and destination, individuals exhibit a variety of behaviors (e.g., a school drop detour, a brief stop at a supermarket). The ability to characterize and compare the variability of individuals' fine-grained movement behavior can greatly support location-based services and smart spatial sampling strategies. We propose a TRip DIversity Measure --TRIM – that quantifies the regularity of users' path choice between an origin and destination. TRIM effectively captures the extent of the diversity of the paths that are taken between a given origin and destination pair, and identifies users with distinct movement patterns, while facilitating the comparison of the movement behavior variations between users. Our experiments using synthetic and real datasets and across geographies show the effectiveness of our method.

Since its introduction a decade ago, differential privacy has been deployed and adapted in different application scenarios due to its rigorous protection of individuals' privacy regardless of the adversary's background knowledge. An urgent open research issue is how to query/release time evolving datasets in a differentially private manner. Most of the proposed solutions in this area focus on releasing private counters or histograms, which involve low sensitivity, and the main focus of these solutions is minimizing the amount of noise and the utility loss throughout the process. In this paper we consider the case of releasing private numerical values with unbounded sensitivity in a dataset that grows over time. While providing utility bounds for such case is of particular interest, we show that straightforward application of current mechanisms cannot guarantee (differential) privacy for individuals under an open-world assumption where data is continuously being updated, especially if the dataset is updated by an outlier.

People are increasingly volunteering personal data. Services based on this data rely on a high number of participants and high data quality. Personal data is often seen as private and individuals are more likely to provide such data if they can choose its granularity, e.g., instead of an exact value, they may provide a range. Focusing on spatial crowdsourced data, this work aims to determine whether the common method of coarsening location data of privacy-conscious individuals is an effective approach if fine-grained location data has also been submitted by privacy-apathetic users. We propose a novel inference attack to refine the location of privacy-conscious individuals. Our experiments suggest that even with a dataset that is mostly populated with privacy-conscious users, our technique succeeds with high precision and recall.

Trajectory data does not only show the location of users over a period of time, but also reveals a high level of detail regarding their lifestyle, preferences and habits. Hence, it is highly susceptible to privacy concerns. Trajectory privacy has become a key research topic when sharing/exchanging trajectory datasets. Most existing studies focus on protecting trajectory data through obfuscating, anonymising or perturbing the data with the aim to maximize user privacy. Although such approaches appear plausible, our work suggests that precise trajectory information can be inferred even from other sources of data. We consider the case in which a location service provider only shares POI query results of users with third parties instead of exchanging users' raw trajectory data to preserve privacy. We develop an inference algorithm and show that it can effectively approximate original trajectories using solely the POI query results.

Privacy concerns place a great impediment to publishing and/or exchanging trajectory data across companies and institutions. This has urged researchers to address privacy issues prior to trajectory data release. Currently, privacy preserving solutions distort original data unnecessarily, hence, degrade data utility and make such data less useful for third parties. We consider a trajectory as a sequence of stops and moves, and propose an approach that exploits features of a trajectory as means for preserving privacy while maintaining a high level of utility. We introduce the concept of sensitivity for stops based on the assumption that they are more vulnerable to privacy threats. We propose an efficient algorithm that either substitutes sensitive stop points of a trajectory with moves from the same trajectory or introduces a minimal detour if a less sensitive stop can not be found on the same route. Our experiments shows that our method balances user privacy and data utility: it protects privacy through preventing an adversary from making inferences about sensitive stops while maintaining a high level of data similarity to the original dataset.