A Survey on IT-Techniques for a Dynamic Emergency Management in Large Infrastructures

This deliverable is a survey on the IT techniques that are relevant to the three use cases of the project EMILI. It describes the state-of-the-art in four complementary IT areas: Data cleansing, supervisory control and data acquisition, wireless sensor networks and complex event processing. Even though the deliverable’s authors have tried to avoid a too technical language and have tried to explain every concept referred to, the deliverable might seem rather technical to readers so far little familiar with the techniques it describes

Intelligent monitoring and fault diagnosis for ATLAS TDAQ: a complex event processing solution

Effective monitoring and analysis tools are fundamental in modern IT infrastructures to get insights on the overall system behavior and to deal promptly and effectively with failures. In recent years, Complex Event Processing (CEP) technologies have emerged as effective solutions for information processing from the most disparate fields: from wireless sensor networks to financial analysis. This thesis proposes an innovative approach to monitor and operate complex and distributed computing systems, in particular referring to the ATLAS Trigger and Data Acquisition (TDAQ) system currently in use at the European Organization for Nuclear Research (CERN). The result of this research, the AAL project, is currently used to provide ATLAS data acquisition operators with automated error detection and intelligent system analysis. The thesis begins by describing the TDAQ system and the controlling architecture, with a focus on the monitoring infrastructure and the expert system used for error detection and automated recovery. It then discusses the limitations of the current approach and how it can be improved to maximize the ATLAS TDAQ operational efficiency. Event processing methodologies are then laid out, with a focus on CEP techniques for stream processing and pattern recognition. The open-source Esper engine, the CEP solution adopted by the project is subsequently analyzed and discussed. Next, the AAL project is introduced as the automated and intelligent monitoring solution developed as the result of this research. AAL requirements and governing factors are listed, with a focus on how stream processing functionalities can enhance the TDAQ monitoring experience. The AAL processing model is then introduced and the architectural choices are justified. Finally, real applications on TDAQ error detection are presented. The main conclusion from this work is that CEP techniques can be successfully applied to detect error conditions and system misbehavior. Moreover, the AAL project demonstrates a real application of CEP concepts for intelligent monitoring in the demanding TDAQ scenario. The adoption of AAL by several TDAQ communities shows that automation and intelligent system analysis were not properly addressed in the previous infrastructure. The results of this thesis will benefit researchers evaluating intelligent monitoring techniques on large-scale distributed computing system

Complex Event Processing Modeling by Prioritized Colored Petri Nets

Complex event processing (CEP) is a technology that allows us to process and correlate large volumes of data by using event patterns, aiming at promptly detecting specific situations that could require special treatment. The event types and event patterns for a particular application domain are implemented by using an event processing language (EPL). Although some current model-driven tools allow end users to easily define these patterns, which are then transformed automatically into a particular EPL, the generated code is syntactically but not semantically validated. To deal with this problem, a prioritized colored Petri net (PCPN) model for CEP is proposed and conducted in this paper. This well-known graphical formalism together with CPNTools makes possible the modeling, simulation, analysis, and semantic validation of complex event-based systems. To illustrate this approach, a case study is presented, as well as a discussion on the benefits from using PCPN for modeling CEP-based systems.El procesamiento de eventos complejos (CEP) es una tecnología que nos permite procesar y correlacionar grandes volúmenes de datos utilizando patrones de eventos, con el objetivo de detectar rápidamente situaciones específicas que podrían requerir un tratamiento especial. Los tipos de eventos y patrones de eventos para un dominio de aplicación particular se implementan utilizando un lenguaje de procesamiento de eventos (EPL). Aunque algunas herramientas actuales impulsadas por modelos permiten a los usuarios finales definir fácilmente estos patrones, que luego se transforman automáticamente en un EPL particular, el código generado se valida sintácticamente pero no semánticamente. Para abordar este problema, en este documento se propone y lleva a cabo un modelo de red de Petri coloreada y priorizada (PCPN) para CEP. Este formalismo gráfico bien conocido junto con CPNTools hace posible la modelización, simulación, análisis y validación semántica de sistemas basados en eventos complejos. Para ilustrar este enfoque, se presenta un estudio de caso, así como una discusión sobre los beneficios de usar PCPN para modelar sistemas basados en CEP.This work was supported in part by the Spanish Ministry of Science and Innovation and the European Union FEDER Funds with the Project DArDOS entitled Formal development and analysis of complex systems in distributed contexts: foundations, tools and applications under Grant TIN2015-65845-C3, subprojects 2-R and 3-R, and the Research Network on Services Science and Engineering under Grant TIN2014-53986-REDT, and in part by the University of Cádiz under Project PR2016-032

Intelligent event broker: a complex event processing system in big data contexts

In Big Data contexts, many batch and streaming oriented technologies have emerged to deal with the high valuable sources of events, such as Internet of Things (IoT) platforms, the Web, several types of databases, among others. The huge amount of heterogeneous data being constantly generated by a world of interconnected things and the need for (semi)-automated decision-making processes through Complex Event Processing (CEP) and Machine Learning (ML) have raised the need for innovative architectures capable of processing events in a streamlined, scalable, analytical, and integrated way. This paper presents the Intelligent Event Broker, a CEP system built upon flexible and scalable Big Data techniques and technologies, highlighting its system architecture, software packages, and classes. A demonstration case in Bosch’s Industry 4.0 context is presented, detailing how the system can be used to manage and improve the quality of the manufacturing process, showing its usefulness for solving real-world event-oriented problems.This work has been supported by FCT –Fundação para a Ciência e Tecnologiawithin the Project Scope: UID/CEC/00319/2019 and the Doctoral scholarship PD/BDE/135101/2017. This paper uses icons made by Freepik, from www.flaticon.com

SISTEM INFORMASI KEPEGAWAIAN DAN AKADEMIK DI SMA NEGERI 19 SURABAYA BERBASIS PHP

ABSTRACT SMA Negeri 19 Surabaya is one of many high school, which is appointed as the best school of north Surabaya area, which have complex process in managing they data. Event of the staffing subsection that manage all staffs data and processing all staffing rules. Now, in managing staffing data still use manual system that not computerized, not distributed in saving data and not well documentation. Where in managind the data still use Microsoft Word and Microsoft excel, doesn�t have a database for savibng data, data sharing doesn�t use network but use printout hardcopy. Therefore, the last project �Staffing Information System and academic of SMA Negeri 19 Surabaya PHP Based� becomes media to managing staffing data in staffing subsection covering staff data management, and other staffing administrations and many components to support this web application. The last project are built with use software engineering method is waterfall, and implementation use PHP programming language, and processing of database use MySQL. Keywords : staffing information system and academic PHP based

Pervasive Monitoring - An Intelligent Sensor Pod Approach for Standardised Measurement Infrastructures

Geo-sensor networks have traditionally been built up in closed monolithic systems, thus limiting trans-domain usage of real-time measurements. This paper presents the technical infrastructure of a standardised embedded sensing device, which has been developed in the course of the Live Geography approach. The sensor pod implements data provision standards of the Sensor Web Enablement initiative, including an event-based alerting mechanism and location-aware Complex Event Processing functionality for detection of threshold transgression and quality assurance. The goal of this research is that the resultant highly flexible sensing architecture will bring sensor network applications one step further towards the realisation of the vision of a “digital skin for planet earth”. The developed infrastructure can potentially have far-reaching impacts on sensor-based monitoring systems through the deployment of ubiquitous and fine-grained sensor networks. This in turn allows for the straight-forward use of live sensor data in existing spatial decision support systems to enable better-informed decision-making.Seventh Framework Programme (European Commission) (FP7 project GENESIS no. 223996)Austria. Federal Ministry of Transport, Innovation and TechnologyERA-STAR Regions Project (G2real)Austria. Federal Ministry of Science and Researc

Towards analytical provenance visualization for criminal intelligence analysis

In criminal intelligence analysis to complement the information entailed and to enhance transparency of the operations, it demands logs of the individual processing activities within an automated processing system. Management and tracing of such security sensitive analytical information flow originated from tightly coupled visualizations into visual analytic system for criminal intelligence that triggers huge amount of analytical information on a single click, involves design and development challenges. To lead to a believable story by using scientific methods, reasoning for getting explicit knowledge of series of events, sequences and time surrounding interrelationships with available relevant information by using human perception, cognition, reasoning with database operations and computational methods, an analytic visual judgmental support is obvious for criminal intelligence. Our research outlines the requirements and development challenges of such system as well as proposes a generic way of capturing different complex visual analytical states and processes known as analytic provenance. The proposed technique has been tested into a large heterogeneous event-driven visual analytic modular analyst’s user interface (AUI) of the project VALCRI (Visual Analytics for Sensemaking in Criminal Intelligence) and evaluated by the police intelligence analysts through it’s visual state capturing and retracing interfaces. We have conducted several prototype evaluation sessions with the groups of end-users (police intelligence analysts) and found very positive feedback. Our approach provides a generic support for visual judgmental process into a large complex event-driven AUI system for criminal intelligence analysi

A Heuristically Optimized Complex Event Processing Engine for Big Data Stream Analytics

This paper describes a Big Data stream analytics platform developed within the DEWI project for processing upcoming events from wireless sensors installed in a truck. The platform consists of a Complex Event Processing (CEP) engine capable of triggering alarms from a predefined set of rules. In general these rules are characterized by multiple parameters, for which finding their opti- mal value usually yields a challenging task. In this paper we explain a methodol- ogy based on a meta-heuristic solver that is used as a wrapper to obtain optimal parametric rules for the CEP engine. In particular this approach optimizes CEP rules through the refinement of the parameters controlling their behavior based on an alarm detection improvement criterion. As a result the proposed scheme retrieves the rules parameterized in a detection-optimal fashion. Results for a cer- tain use case – i.e. fuel level of the vehicle – are discussed towards assessing the performance gains provided by our method

Application of the Complex Event Processing system for anomaly detection and network monitoring

Protection of infrastructures for e-science, including grid environments and NREN facilities, requires the use of novel techniques for anomaly detection and network monitoring. The aim is to raise situational awareness and provide early warning capabilities. The main operational problem that most network operators face is integrating and processing data from multiple sensors and systems placed at critical points of the infrastructure. From a scientific point of view, there is a need for the efficient analysis of large data volumes and automatic reasoning while minimizing detection errors. In this article, we describe two approaches to Complex Event Processing used for network monitoring and anomaly detection and introduce the ongoing SECOR project (Sensor Data Correlation Engine for Attack Detection and Support of Decision Process), supported by examples and test results. The aim is to develop methodology that allows for the construction of next-generation IDS systems with artificial intelligence, capable of performing signature-less intrusion detection