We define and solve the problem of event detection and delineation as a task of identifying events and decomposing them to their major sub-events, with a description and a timeline. We propose DeLi, an algorithm that focuses on providing such an understanding of events and sub-events. DeLi, to the best of our knowledge, is the first method that addresses the problem in a generic stream of text, and in an online fashion. Extensive evaluation on social streaming data demonstrates that, by combining the structure of a social network with content attributes, our method outperforms the state-of-the-art techniques.

Social networks have become the de facto online resource for people to share, comment on and be informed about events pertinent to their interests and livelihood, ranging from road traffic or an illness to concerts and earthquakes, to economics and politics. This has been the driving force behind research endeavors that analyse such data. In this paper, we focus on how Content Networks can help us identify events effectively. Content Networks incorporate both structural and content-related information of a social network in a unified way, at the same time, bringing together two disparate lines of research: graph-based and content-based event discovery in social media. We model interactions of two types of nodes, users and content, and introduce an algorithm that builds heterogeneous, dynamic graphs, in addition to revealing content links in the network's structure. By linking similar content nodes and tracking connected components over time, we can effectively identify different types of events. Our evaluation on social media streaming data suggests that our approach outperforms state-of-the-art techniques, while showcasing the significance of hidden links to the quality of the results.

In any competitive business, success is based on the ability to make an item more appealing to customers than the competition. A number of questions arise in the context of this task: how do we formalize and quantify the competitiveness between two items? Who are the main competitors of a given item? What are the features of an item that most affect its competitiveness? Despite the impact and relevance of this problem to many domains, only a limited amount of work has been devoted toward an effective solution. In this paper, we present a formal definition of the competitiveness between two items, based on the market segments that they can both cover. Our evaluation of competitiveness utilizes customer reviews, an abundant source of information that is available in a wide range of domains. We present efficient methods for evaluating competitiveness in large review datasets and address the natural problem of finding the top-k competitors of a given item. Finally, we evaluate the quality of our results and the scalability of our approach using multiple datasets from different domains.

Top - k dominating queries combine the natural idea of selecting the k best items with a comprehensive “goodness” criterion based on dominance. A point p 1 dominates p 2 if p 1 is as good as p 2 in all attributes and is strictly better in at least one. Existing works address the problem in settings where data objects are multidimensional points. However, there are domains where we only have access to the distance between two objects. In cases like these, attributes reflect distances from a set of input objects and are dynamically generated as the input objects change. Consequently, prior works from the literature cannot be applied, despite the fact that the dominance relation is still meaningful and valid. For this reason, in this work, we present the first study for processing top- k dominating queries over distance-based dynamic attribute vectors, defined over a metric space . We propose four progressive algorithms that utilize the properties of the underlying metric space to efficiently solve the problem and present an extensive, comparative evaluation on both synthetic and real-world datasets.

The availability of large volumes of interaction data and scalable data mining techniques have made possible to study the online behaviour for millions of Web users. Part of the efforts have focused on understanding how users interact and engage with web content. However, the measurement of within-content engagement remains a difficult and unsolved task. This is because of the lack of standardised, well-validated methods for measuring engagement, especially in an online context. To address this gap, we perform a controlled user study where we observe how users respond to online news in the presence or lack of interest. We collect mouse tracking data, which are known to correlate with visual attention, and examine how cursor behaviour can inform user engagement measures. The proposed method does not use any pre-determined concepts to characterise the cursor patterns. We, rather, follow an unsupervised approach and use a large set of features engineered from our data to extract the cursor patterns. Our findings support the connection between gaze and cursor behaviour but also, and more importantly, reveal other dependencies, such as the correlation between cursor activity and experienced affect. Finally, we demonstrate the value of our method by predicting the outcome of online news reading experiences.

Top-k dominating queries combine the natural idea of se-lecting the k best items with a comprehensive \\goodness" criterion based on dominance. A point p1 dominates p2 if p1 is as good as p2 in all attributes and is strictly better in at least one. Existing works address the problem in settings where data objects are multidimensional points. However, there are domains where we only have access to the dis-tance between two objects. In cases like these, attributes re ect distances from a set of input objects and are dynam-ically generated as the input objects change. Consequently, prior works from the literature can not be applied, despite the fact that the dominance relation is still meaningful and valid. For this reason, in this work, we present the rst study for processing top-k dominating queries over distance-based dynamic asttribute vectors, dened over a metric space. We propose four progressive algorithms that utilize the proper-ties of the underlying metric space to eciently solve the problem, and present an extensive, comparative evaluation on both synthetic and real world data sets.

Wireless sensor networks enable cost-effective data collection for tasks such as precision agriculture and environment monitoring. However, the resource-constrained nature of sensor nodes, which often have both limited computational capabilities and battery lifetimes, means that applications that use them must make judicious use of these resources. Research that seeks to support data intensive sensor applications has explored a range of approaches and developed many different techniques, including bespoke algorithms for specific analyses and generic sensor network query processors. However, all such proposals sit within a multi-dimensional design space, where it can be difficult to understand the implications of specific decisions and to identify optimal solutions. This paper presents a benchmark that seeks to support the systematic analysis and comparison of different techniques and platforms, enabling both development and user communities to make well informed choices. The contributions of the paper include: (i) the identification of key variables and performance metrics; (ii) the specification of experiments that explore how different types of task perform under different metrics for the controlled variables; and (iii) an application of the benchmark to investigate the behavior of several representative platforms and techniques.

Microblogging platforms, such as Twitter, Tumblr etc., have been established as key components in the contemporary Web ecosystem. Users constantly post snippets of information regarding their actions, interests or perception of their surroundings, which is why they have been attributed the term Live Web. Nevertheless, research on such platforms has been quite limited when it comes to identifying events, but is rapidly gaining ground. Event identification is a key step to news reporting, proactive or reactive crisis management at multiple scales, efficient resource allocation, etc. In this paper, we focus on the problem of automatically identifying events as they occur, in such a user-driven, fast paced and voluminous setting. We propose a novel and natural way to address the issue using notions from emotional theories, combined with spatiotemporal information and employ online event detection mechanisms to solve it at large scale in a distributed fashion. We present a modular framework that incorporates all of our key ideas and experimentally validate its superiority, in terms of both efficiency and effectiveness, over the state-of-the-art using real life data from the Twitter stream. We also present empirical evidence on the importance of spatiotemporal information in event detection for this setting.

Skyline queries have attracted considerable attention by the database community during the last decade, due to their applicability in a series of domains. However, most existing works tackle the problem from an efficiency standpoint, i.e., returning the skyline as quickly as possible. The user is then presented with the entire skyline set, which may be in several cases overwhelming, therefore requiring manual inspection to come up with the most informative data points. To overcome this shortcoming, we propose a novel approach in selecting the k most diverse skyline points, i.e., the ones that best capture the different aspects of both the skyline and the dataset they belong to. We present a novel formulation of diversification which, in contrast to previous proposals, is intuitive, because it is based solely on the domination relationships among points. Consequently, additional artificial distance measures (e.g., Lp norms) among skyline points are not required. We present efficient approaches in solving this problem and demonstrate the efficiency and effectiveness of our approach through an extensive experimental evaluation with both real-life and synthetic data sets.

The vision of autonomic computing involves distributed nodes capable of managing and preserving themselves. In practice, autonomic computing is also strongly connected to the concepts of mobility and platform heterogeneity since next generation networks assume different types of mobile nodes that operate inside ad hoc environments. In such cases where the context changes can be frequent, the capability of capturing the environmental changes is crucial. However, it cannot be assumed that all the nodes are equipped with all possible types of sensors in order to locally retrieve the required contextual information. Sensor information exchange between the nodes through efficient data dissemination mechanisms can further improve the overall awareness of the network. Another challenging task for autonomic computing concerns the self-reconfiguration of the autonomous nodes according to the sensed context. This paper presents the overall architecture design, the implementation and the evaluation of a middleware developed in the context of the Integrated Platform for Autonomic Computing (IPAC). This middleware is targeted to embedded devices and supports mobile context-aware applications that render the aforementioned desired behavior. The paper focuses on two aspects of autonomic computing: collaborative context-awareness and self-reconfiguration.

In any competitive business, success is based on the ability to make an item more appealing to customers than the competition. A number of questions arise in the context of this task: how do we formalize and quantify the competitiveness relationship between two items? Who are the true competitors of a given item? What are the features of an item that most affect its competitiveness? Despite the impact and relevance of this problem to many domains, only a limited amount of work has been devoted toward an effective solution. In this paper, we present a formal definition of the competitiveness between two items. We present efficient methods for evaluating competitiveness in large datasets and address the natural problem of finding the top-k competitors of a given item. Our methodology is evaluated against strong baselines via a user study and experiments on multiple datasets from different domains.

Sensor Networks have received considerable attention recently, as they provide manifold benefits. Not only are they a means for data acquisition and monitoring of unexplored or inaccessible areas, they are also a low-cost alternative for sensing the environment, which greatly aids to better understand our surroundings. A major motivation in either occasion is to acknowledge endangering situations and take action(s) accordingly. To this end, we would like to enable data mining or analysis techniques on top or, even better, within such networks, due to the prohibitive cost of communication in this setting. In this work, we demonstrate running data mining algorithms on a set of sensors, which are of low-processing power. In addition to showcasing the execution of data analysis algorithms on resource-constrained hardware, our demo is intended to show how to take advantage of the properties of each algorithm to make better use of the sensors and their capabilities. We support the execution and monitoring of these algorithms with a graphical user interface (GUI).