Event sequence processing

The rapidly changing data requirements of today's dynamic business environments are not handled well by current online analytical processing (OLAP) systems. Physically integrating data from new sources into OLAP systems is a long and time-consuming process, making logical integration the better choice in many situations. The increasing use of Extended Markup Language (XML) suggests that the required external data will most often be available in XML format. We present a theoretically well-founded approach to the logical federation of OLAP and XML data sources. The approach allows external XML data to be presented along with dimensional data in OLAP query results and enables the use of external XML data for selection and grouping. Special care is taken to ensure that semantic problems do not occur in the integration process. This opens up many new application areas for OLAP. A number of effective optimization techniques for OLAP-XML federations are presented. Performance results from the prototype implementation show that the approach is an attractive alternative to physical integration. The approach is exemplified using a real-world case study from the B2B domain.

University of Edinburgh, School of Informatics, EH8 9AB, Edinburgh, Scotland, United Kingdom ; cristinutza0107@yahoo.com Abstract. This paper discusses the problem of multidimensional databases, with the purpose of demonstrating the efficient support offered by them for the management of OLAP processing and motivation of decisions, especially the ones related to environmental risks. The OLAP (On-Line Analytical Processing) systems that implement or use multidimensional databases are oriented towards decision makers and allow them to an easily process, sectioning and grouping of data, as well as the visualization of data from different perspectives, for the discovery of the factors that would influence a certain behavior. OLAP systems offer advanced statistical analysis facilities, such as the one based on time series or forecasts, which are based on predefined models. The calculation of the environmental risk offers an image of the degree of air pollution due to specific polluting factors, obtained especially as result of the industrial processes that have taken place in the Targoviste, in Dambovita area. An OLAP system, consisting of decision support components, was implemented as part of the PN2 three years research project: “Sustainable Management System of Resources Used for Monitoring and Evaluating the Environmental Risks in Order to Prevent the Negative Effects and to Manage Crises Situations - MEMDUR”, code D11-037/18.09.2007. Keywords: environmental risks, multidimensional databases, OLAP systems, SOLAP architecture, MEMDUR project.

Data warehouses provide data for on-line analytical processing (OLAP) systems, which deal with analytical tasks in businesses. As these tasks do not depend on the latest updates by transactions, the input from operational databases is separated from the outputs to dialogue interfaces for OLAP. In this paper a layered formal specification for data warehouses and OLAP systems using Abstract State Machines (ASMs) is presented. The approach explicitly exploits the fundamental idea of separating input from operational databases and output to OLAP systems. Then it will be shown how this specification can be extended to distributed data warehouses.KeywordsData WarehouseOperational DatabaseAbstract State MachineStar SchemaTional DatabaseThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This study presents a novel method to develop kleene operators on a Binary Tree-Based Directed Graph (BTDG) to improve expressibility and performance of our developed complex event processing system. Complex Event Processing (CEP) systems are widely employed to notify opportunities and threats, which many of these situations are discovered through detection of multiple occurrences of the similar set of events. Many of CEP systems proposed kleene operators to detect these situations. The high expressibility of our implemented CEP system achieved by developing algorithms on kleene operator. These algorithms are designed on a BTDG as a unified basis to arrange input data and joining this input according to a given query. The performance is improved through directing events instantly to join with other events, dynamically switching to an optimal plan and late applying combination for kleene with specified size. The experiments shows kleene operator on BTDG brings rather high degree of expressiblity and performance in compare to its counterparts.

The paper presents the idea of using and application of on-line analytical processing (OLAP) systems in monitoring of electrical power engineering devices. Data saved in central repository for historical data are submitted through online working analytic systems. At the moment of addition of new data happens process of definition the mutual correlation between the new and already existing data. This leads to the analysis based on OLAP systems. The hereby paper describes the advantages of OLAP systems in aspect of using them in monitoring of electrical power engineering devices.

In the context of scientometrics and bibliometrics, several research fields are dealing with bibliographic data. In this paper, we will explore how the combination of online analytical processing (OLAP) analysis and information networks could be an interesting issue. In Business Intelligence, OLAP is a technology supported by data warehousing systems. It provides tools for analyzing data according to multiple dimensions and multiple hierarchical levels. At the same time, several information networks (co-authors network, citations network, institutions network, etc.) can be built based on bibliographic databases. Originally, OLAP was introduced to analyze structured data. However, in this paper, we wonder if, by combining OLAP and information networks, we can provide a new way of analyzing bibliographic data. OLAP should be able to handle information networks and be also useful for monitoring, browsing and analyzing the content and the structure of bibliographic networks. The goal of this survey paper is to review previous work on OLAP and information networks dealing with bibliographic data. We also propose a comparison between traditional OLAP and OLAP on information networks and discuss the challenges OLAP faces regarding bibliographic networks.

Complex event processing (CEP) systems have gained significant importance in various domains, such as finance, logistics, and security, where the real-time analysis of event streams is crucial. However, as the volume and complexity of event data continue to grow, optimizing the performance of CEP systems becomes a critical challenge. This paper investigates the impact of indexing strategies on the performance of databases handling complex event processing. We propose a novel indexing technique, called Hierarchical Temporal Indexing (HTI), specifically designed for the efficient processing of complex event queries. HTI leverages the temporal nature of event data and employs a multi-level indexing approach to optimize query execution. By combining temporal indexing with spatial- and attribute-based indexing, HTI aims to accelerate the retrieval and processing of relevant events, thereby improving overall query performance. In this study, we evaluate the effectiveness of HTI by implementing complex event queries on various CEP systems with different indexing strategies. We conduct a comprehensive performance analysis, measuring the query execution times and resource utilization (CPU, memory, etc.), and analyzing the execution plans and query optimization techniques employed by each system. Our experimental results demonstrate that the proposed HTI indexing strategy outperforms traditional indexing approaches, particularly for complex event queries involving temporal constraints and multi-dimensional event attributes. We provide insights into the strengths and weaknesses of each indexing strategy, identifying the factors that influence performance, such as data volume, query complexity, and event characteristics. Furthermore, we discuss the implications of our findings for the design and optimization of CEP systems, offering recommendations for indexing strategy selection based on the specific requirements and workload characteristics. Finally, we outline the potential limitations of our study and suggest future research directions in this domain.

The traditional OLAP (On-Line Analytical Processing) systems store data in relational databases. Unfortunately, it is difficult to manage big data volumes with such systems. As an alternative, NoSQL systems (Not-only SQL) provide scalability and flexibility for an OLAP system. We define a set of rules to map star schemas and its optimization structure, a precomputed aggregate lattice, into two logical NoSQL models: column-oriented and document-oriented. Using these rules we analyse and implement two decision support systems, one for each model (using MongoDB and HBase).We compare both systems during the phases of data (generated using the TPC-DS benchmark) loading, lattice generation and querying.

Goal of Complex Event Processing (CEP) is to identify meaningful events (such as opportunities or threats) and respond to them as quickly as possible. Traditional CEP frameworks do not have built-in security features. Any intruder may enter into the system and may send incorrect data to CEP engine. It causes to miss some patterns or it may lead to wrong prediction of patterns. Traditional CEP systems can be exploited through one or the other way. CEP system malfunctions when it is exploited. There is no built in procedure to check the health status of traditional CEP systems. Traditional CEP systems do not have procedures to take appropriate actions when system is affected or going to affect. When CEP system identifies some patterns, it will alert corresponding personnel about the occurrence of the event. Sometimes alert message may be undelivered or unnoticed by the corresponding people. Traditional CEP systems will not provide delivery assurance of alert message. These are few security challenges to existing CEP systems. In this paper Secure Complex Event Processing Framework is proposed to address above security challenges. Proposed framework is generic and it is pluggable into any existing CEP systems.

SUMMARYRecently, event processing (EP) has gained considerable attention as an individual discipline in computer science. From a software engineering perspective, EP systems still lack the maturity of well‐established software architectures. For the development of industrial EP systems, generally accepted software architectures based on proven design patterns and principles are still missing. In this article, we introduce a catalog of design patterns that supports the development of event‐driven architectures (EDAs) and complex EP systems. The design principles originate from experiences reported in publications as well as from our own experiences in building EP systems with industrial and academic partners. We present several patterns on different layers of abstractions that define the overall structure as well as the building blocks for EP systems. Architectural patterns that determine the top‐level structure of an EDA can be distinguished from design patterns that specify the basic mechanisms of EP. The practical application of the catalog of patterns is described by the pattern‐based design of a sample EDA for a sensor‐based energy control system. Finally, we propose a coherent and general reference architecture for EP derived from the proposed patterns.Copyright © 2013 John Wiley & Sons, Ltd.

The nature of data in enterprises and on the Internet is changing. Data used to be stored in a database first and queried later. Today timely processing of new data, represented as events, is increasingly valuable. In many domains, complex event processing (CEP) systems detect patterns of events for decision making. Examples include processing of environmental sensor data, trades in financial markets and RSS web feeds. Unlike conventional database systems, most current CEP systems pay little attention to query optimisation. They do not rewrite queries to more efficient representations or make decisions about operator distribution, limiting their overall scalability. This paper describes the Next CEP system that was especially designed for query rewriting and distribution. Event patterns are specified in a high-level query language and, before being translated into event automata, are rewritten in a more efficient form. Automata are then distributed across a cluster of machines for detection scalability. We present algorithms for query rewriting and distributed placement. Our experiments on the Emulab test-bed show a significant improvement in system scalability due to rewriting and distribution. 1.

Complex event processing (CEP) over event streams has become increasingly important for real-time applications ranging from health care, supply chain management to business intelligence. These monitoring applications submit complex event queries to track sequences of events that match a given pattern. As these systems mature the need for increasingly complex nested sequence query support arises, while the state-of-art CEP systems mostly support the execution of only flat sequence queries. In this paper, we introduce our nested CEP query language NEEL for expressing nested queries composed of sequence, negation, AND and OR operators. Thereafter, we also define its formal semantics. Subtle issues with negation and predicates within the nested sequence context are discussed. An E-Analytics system for processing nested CEP queries expressed in the NEEL language has been developed. Lastly, we demonstrate the utility of this technology by describing a case study of applying this technology to a real-world application in health care.

8 - Multidimensional clustering

Models of multi-dimensional analysis for qualitative data and its application

In such areas as transport, healthcare, business process management, financial services, complex event processing (CEP) systems are becoming in demand. Large volumes of streams of events are generated and collected by these systems. Real-time processing of event stream data is essential. Based on current and past events, a decision is made, and forecasting future events is an alternative goal. For the fastest reaction to non-standard behavior, it is necessary to process data streams in real time, it means, as quickly as possible. That's why it is important to find new technologies which can speed up data processing. In this article expensive queries in CEP is identified, effective optimizations to improve their performance significantly is proposed. It is developed and implemented algorithm, which should decrease processing time. The results are compared with state-of-the-art CEP system.