Node Classification in Uncertain Graphs

In many real applications that use and analyze networked data, the links in the network graph may be erroneous, or derived from probabilistic techniques. In such cases, the node classification problem can be challenging, since the unreliability of the links may affect the final results of the classification process. If the information about link reliability is not used explicitly, the classification accuracy in the underlying network may be affected adversely. In this paper, we focus on situations that require the analysis of the uncertainty that is present in the graph structure. We study the novel problem of node classification in uncertain graphs, by treating uncertainty as a first-class citizen. We propose two techniques based on a Bayes model and automatic parameter selection, and show that the incorporation of uncertainty in the classification process as a first-class citizen is beneficial. We experimentally evaluate the proposed approach using different real data sets, and study the behavior of the algorithms under different conditions. The results demonstrate the effectiveness and efficiency of our approach

Real-Time Data Analytics in Sensor Networks

Abstract. The proliferation of Wireless Sensor Networks (WSNS) in the past decade has provided the bridge between the physical and digital worlds, enabling the monitoring and study of physical phenomena at a granularity and level of detail that was never before possible. In this study, we review the efforts of the research community with respect to two important problems in the context of WSNS: real-time collection of the sensed data, and real-time processing of these data series

Report on the First International Workshop on Personal Data Analytics in the Internet of Things (PDA@IOT 2014)

International audienceThe 1st International Workshop on Personal Data Analytics in the Internet of Things (PDA@IOT), held in conjunction with VLDB 2014, aims at sparking research on data analytics, shifting the focus from business to consumers services. While much of the public and academic discourse about personal data has been dominated by a focus on the privacy concerns and the risks they raise to the individual, especially when they are seen as the new oil of the global economy. PDA@IOT focus on how persons could effectively exploit the data they massively create in CyberPhysicalworlds. We believe that the full potential of the IoT goes far beyond connecting “things” to the Internet: it is about using data to create new value for people. In a People-centric computing paradigm, both small scalepersonal data and large scale aggregated data should be exploited to identify unmet needs and proactively offerthem to users. PDA@IOT seeks to address current technology barriers that impede existing personal dataprocessing and analytics solutions to empower people in personal decision making.The PDA@IOT ambition is to provide a unique forum for researchers and practitioners that approach personal data from different angles, ranging from data management and processing, to data mining and human-data interaction, as well as to nourish the interdisciplinary synergies required to tackle the challenges and problems emerging in People-centric Computing

FreSh: A Lock-Free Data Series Index

We present FreSh, a lock-free data series index that exhibits good performance (while being robust). FreSh is based on Refresh, which is a generic approach we have developed for supporting lock-freedom in an efficient way on top of any localityaware data series index. We believe Refresh is of independent interest and can be used to get well-performed lock-free versions of other locality-aware blocking data structures. For developing FreSh, we first studied in depth the design decisions of current state-of-the-art data series indexes, and the principles governing their performance. This led to a theoretical framework, which enables the development and analysis of data series indexes in a modular way. The framework allowed us to apply Refresh, repeatedly, to get lock-free versions of the different phases of a family of data series indexes. Experiments with several synthetic and real datasets illustrate that FreSh achieves performance that is as good as that of the state-of-the-art blocking in-memory data series index. This shows that the helping mechanisms of FreSh are light-weight, respecting certain principles that are crucial for performance in locality-aware data structures.This paper was published in SRDS 2023.Comment: 12 pages, 18 figures, Conference: Symposium on Reliable Distributed Systems (SRDS 2023

Conditional heavy hitters : detecting interesting correlations in data streams

The notion of heavy hitters—items that make up a large fraction of the population—has been successfully used in a variety of applications across sensor and RFID monitoring, network data analysis, event mining, and more. Yet this notion often fails to capture the semantics we desire when we observe data in the form of correlated pairs. Here, we are interested in items that are conditionally frequent: when a particular item is frequent within the context of its parent item. In this work, we introduce and formalize the notion of conditional heavy hitters to identify such items, with applications in network monitoring and Markov chain modeling. We explore the relationship between conditional heavy hitters and other related notions in the literature, and show analytically and experimentally the usefulness of our approach. We introduce several algorithm variations that allow us to efficiently find conditional heavy hitters for input data with very different characteristics, and provide analytical results for their performance. Finally, we perform experimental evaluations with several synthetic and real datasets to demonstrate the efficacy of our methods and to study the behavior of the proposed algorithms for different types of data