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Summarization: FlinkCEP is the Complex Event Processing (CEP) API of the Flink Big Data platform. The high expressive power of the language of FlinkCEP comes at the cost of cumbersome parameterization of the queried patterns, acting as a barrier for FlinkCEP's adoption. Moreover, properly configuring a FlinkCEP program to run over a computer cluster requires advanced skills on modern hardware administration which non-expert programmers do not possess. In this work (i) we build a novel, logical CEP operator that receives CEP pattern queries in the form of extended regular expressions and seamlessly re-writes them to FlinkCEP programs, (ii) we build a CEP Optimizer that automatically decides good job configurations for these FlinkCEP programs. We also present an experimental evaluation which demonstrates the significant benefits of our approach. Παρουσιάστηκε στο: 30th ACM International Conference on Information & Knowledge Management

Summarization: The concept of event processing is established as a generic computational paradigm in various application fields, ranging from data processing in Web environments, over maritime and transport, to finance and medicine. Events report on state changes of a system and its environment. Complex Event Recognition (CER) in turn, refers to the identification of complex/composite events of interest, which are collections of simple events that satisfy some pattern, thereby providing the opportunity for reactive and proactive measures. Examples include the recognition of attacks in computer network nodes, human activities on video content, emerging stories and trends on the Social Web, traffic and transport incidents in smart cities, fraud in electronic marketplaces, cardiac arrhythmias, and epidemic spread. In each scenario, CER allows to make sense of Big event Data streams and react accordingly. The goal of this tutorial is to provide a step-by-step guide for realizing CER in the Big Data era. To do so, it elaborates on major challenges and describes algorithmic toolkits for optimized manipulation of event streams characterized by high volume, velocity and/or lack of veracity, placing emphasis on distributed CER over potentially heterogeneous (data variety) event sources. Finally, we highlight future research directions in the field. Παρουσιάστηκε στο: 43rd International Conference on Very Large Data Bases

Summarization: The concept of event processing is established as a generic computational paradigm in various application fields. Events report on state changes of a system and its environment. Complex event recognition (CER) refers to the identification of composite events of interest, which are collections of simple, derived events that satisfy some pattern, thereby providing the opportunity for reactive and proactive measures. Examples include the recognition of anomalies in maritime surveillance, electronic fraud, cardiac arrhythmias and epidemic spread. This survey elaborates on the whole pipeline from the time CER queries are expressed in the most prominent languages, to algorithmic toolkits for scaling-out CER to clustered and geo-distributed architectural settings. We also highlight future research directions. Presented on: VLDB Journal

This paper presents HERMES, a prototype DB engine that defines a powerful query language for trajectory databases, which enables the support of mobility-centric applications, such as Location-Based Services (LBS). HERMES extends the data definition and manipulation language of Object-Relational DBMS (ORDBMS) with spatio-temporal semantics and functionality based on advanced spatio-temporal indexing and query processing techniques. Its implementation over two ORDBMS and its utilization in various domains proves the expressive power and applicability of HERMES in different application domains where knowledge regarding mobility data is essential. As a proof-of-concept, in this paper HERMES is applied to a case study related with vehicle traffic analysis, demonstrating its flexibility and usefulness for delivering custom-defined LBS.

Research issues in complex event processing (CEP) emphasizing on query optimization.Cover deterministic probabilistic models, centralized distributed settings.Issues for CEP optimization over Big Data enabling cloud computing platforms.Predictive Analytics and CEP in cloud platforms even with dispersed resource pools. Many Big Data technologies were built to enable the processing of human generated data, setting aside the enormous amount of data generated from Machine-to-Machine (M2M) interactions and Internet-of-Things (IoT) platforms. Such interactions create real-time data streams that are much more structured, often in the form of series of event occurrences. In this paper, we provide an overview on the main research issues confronted by existing Complex Event Processing (CEP) techniques, with an emphasis on query optimization aspects. Our study expands on both deterministic and probabilistic event models and spans from centralized to distributed network settings. In that, we cover a wide range of approaches in the CEP domain and review the current status of techniques that tackle efficient query processing. These techniques serve as a starting point for developing Big Data oriented CEP applications. Therefore, we further study the issues that arise upon trying to apply those techniques over Big Data enabling technologies, as is the case with cloud platforms. Furthermore, we expand on the synergies among Predictive Analytics and CEP with an emphasis on scalability and elasticity considerations in cloud platforms with potentially dispersed resource pools.

Summarization: Wireless sensor networks are becoming increasingly popular for a variety of applications. Users are frequently faced with the surprising discovery that readings produced by the sensing elements of their motes are often contaminated with outliers. Outlier readings can severely affect applications that rely on timely and reliable sensory data in order to provide the desired functionality. As a consequence, there is a recent trend to explore how techniques that identify outlier values based on their similarity to other readings in the network can be applied to sensory data cleaning. Unfortunately, most of these approaches incur an overwhelming communication overhead, which limits their practicality. In this paper we introduce an in-network outlier detection framework, based on locality sensitive hashing, extended with a novel boosting process as well as efficient load balancing and comparison pruning mechanisms. Our method trades off bandwidth for accuracy in a straightforward manner and supports many intuitive similarity metrics. Our experiments demonstrate that our framework can reliably identify outlier readings using a fraction of the bandwidth and energy that would otherwise be required. Presented on: Information Systems

In this demo, we present FERARI, a prototype that enables real-time Complex Event Processing (CEP) for large volume event data streams over distributed topologies. Our prototype constitutes, to our knowledge, the first complete, multi-cloud based end-to-end CEP solution incorporating: a) a user-friendly, web-based query authoring tool, (b) a powerful CEP engine implemented on top of a streaming cloud platform, (c) a CEP optimizer that chooses the best query execution plan with respect to low latency and/or reduced inter-cloud communication burden, and (d) a query analytics dashboard encompassing graph and map visualization tools to provide a holistic picture with respect to the detected complex events to final stakeholders. As a proof-of-concept, we apply FERARI to enable mobile fraud detection over real, properly anonymized, telecommunication data from T-Hrvatski Telekom network in Croatia.

We present FERARI, a prototype for processing voluminous event streams over multi-cloud platforms. At its core, FERARI both exploits the potential for in-situ (intra-cloud) processing and orchestrates inter-cloud complex event detection in a communication-efficient way. At the application level, it includes a user-friendly query authoring tool and an analytics dashboard providing granular reports about detected events. In that, FERARI constitutes, to our knowledge, the first complete end-to-end solution of its kind. In this demo, we apply the FERARI approach on a real scenario from the telecommunication domain.

This chapter presents HERMES, a prototype DB engine that defines a powerful query language for trajectory databases, which enables the support of mobility-centric applications, such as Location-Based Services (LBS). HERMES extends the data definition and manipulation language of Object-Relational DBMS (ORDBMS) with spatio-temporal semantics and functionality based on advanced spatio-temporal indexing and query processing techniques. Its implementation over two ORDBMS and its utilization in various domains proves the expressive power and applicability of HERMES in different application domains where knowledge regarding mobility data is essential. As a proof-of-concept, in this chapter HERMES is applied to a case study related with vehicle traffic analysis, demonstrating its flexibility and usefulness for delivering custom-defined LBS.

Many Big Data technologies were built to enable the processing of human generated data, setting aside the enormous amount of data generated from Machine-to-Machine (M2M) interactions. M2M interactions create real-time data streams that are much more structured, often in the form of series of event occurrences. In this paper, we provide an overview on the main research issues confronted by existing Complex Event Processing (CEP) techniques, as a starting point for Big Data applications that enable the monitoring of complex event occurrences in M2M interactions.

Medical devices play a vital role in the delivery of high quality healthcare with their numbers increasing exponentially during the past fifty years. Although recent technological advancements have led to much more reliable and safer Medical Devices (MDs), potential risks of failure and the associated adverse incidents cannot be ignored. To create a safer environment for the patient in terms of technology, it is necessary to provide rapid and accurate information on adverse events, in order to avoid their repetition. The traditional approach of MD Vigilance based only on official user reports of adverse incidents has been proven inadequate today. A modern approach, also used in this work, to the problem is based on the introduction of additional means, such as data mining, extraction, standardization and codification of the information, from different direct and indirect sources worldwide, and its systematic classification and archiving in dedicated databases. The aim is to timely extract information on potentially hazardous MDs and to make it available where appropriate.

For all healthcare institutions and organizations, patient safety is of the utmost importance. A factor that influences patient safety is the existence (or not) of observed adverse events associated with medical devices. Upon the detection of adverse events, all healthcare providers that own the affected medical devices should be promptly notified. In this paper we present the core of a prototype system for medical devices vigilance and patient safety. We present the architecture of this system, the way that it detects the healthcare providers that need to be notified through an entity matching algorithm, as well as briefly present its user interface.

Location data generated from GPS equipped moving objects are typically collected as streams of spatio-temporal (x,y,t) points that when put together form corresponding {\em trajectories}. Most existing studies focus on building ad-hoc querying, analysis, as well as data mining techniques on formed trajectories. As a prior step, trajectory construction is evidently necessary for mobility data processing and understanding -- including tasks like trajectory data cleaning, compression, and segmentation to identify semantic trajectory episodes like stops (e.g. while sitting and standing) and moves (while jogging, walking, driving etc). However, such methods in the current literature, are typically based on offline procedures, which is not sufficient for real life trajectory applications that rely on timely delivery of computed trajectories to serve real time query answers. Filling this gap, our paper proposes a platform, namely SeTraStream, for real-time semantic trajectory construction. Our online framework is capable of providing real-life trajectory data {\em cleaning}, {\em compression}, {\em segmentation} over streaming movement data.

Wireless sensor networks are becoming increasingly popular for a variety of applications. Users are frequently faced with the surprising discovery that readings produced by the sensing elements of their motes are often contaminated with outliers. Outlier readings can severely affect applications that rely on timely and reliable sensory data in order to provide the desired functionality. As a consequence, there is a recent trend to explore how techniques that identify outlier values can be applied to sensory data cleaning. Unfortunately, most of these approaches incur an overwhelming communication overhead, which limits their practicality. In this paper we introduce an in-network outlier detection framework, based on locality sensitive hashing, extended with a novel boosting process as well as efficient load balancing and comparison pruning mechanisms. Our method trades off bandwidth for accuracy in a straightforward manner and supports many intuitive similarity metrics.