Finding event correlations in federated wireless sensor networks

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Event correlation engines help us find events of interest inside raw sensor data streams and help reduce the data volume, simultaneously. This paper discusses some of the challenges faced in finding event correlations over federated wireless sensor networks (WSNs) including high data volumes, uncertain or missing data, application-specific dependencies and widely varying data ranges and sampling frequencies. Analysisover real geo-tracking data of moving objects confirms some of these challenges. Federation at the data layer above the WSNs is presented as a feasible alternative.TÜBİTAK ; IBM Shared University Research program ; European Commissio

Design and implementation of a cloud computing service for finite element analysis

This paper presents an end-to-end discussion on the technical issues related to the design and implementation of a new cloud computing service for finite element analysis (FEA). The focus is specifically on performance characterization of linear and nonlinear mechanical structural analysis workloads over multi-core and multi-node computing resources. We first analyze and observe that accurate job characterization, tuning of multi-threading parameters and effective multi-core/node scheduling are critical for service performance. We design a “smart” scheduler that can dynamically select some of the required parameters, partition the load and schedule it in a resource-aware manner. We can achieve up to 7.53× performance improvement over an aggressive scheduler using mixed FEA loads. We also discuss critical issues related to the data privacy, security, accounting, and portability of the cloud service.European Commission ; IBM Shared University Research (SUR) program ; TÜBİTAK ; IBM PhD Fellowship awardpost-prin

A novel runtime verification solution for LoT systems

Internet of Things (IoT) systems promise a seamless connected world with machines integrating their services without human intervention. It's highly probable that the entities participating in such autonomous machine to machine interactions are to be provided by different manufactures. Thus, integrating such heterogeneous devices from many providers complicates design and verification of IoT systems at an unprecedented scale. In this paper, we propose a novel runtime verification approach for IoT systems. The contributions of our proposed solution include: exploiting the interactions in message sequence charts (MSC) to specify message exchanges of constrained application protocol-based IoT systems in terms of events, a novel event calculus for formally describing IoT system constraints specified by means of MSCs, and an event processing algebra that uses complex-event processing techniques for detecting failures in the system by monitoring the runtime event occurrences with respect to the system constraints defined by event calculus. We further demonstrate the viability of proposed solution with case studies.Publisher versio

Multivariate sensor data analysis for oil refineries and multi-mode identification of system behavior in real-time

Large-scale oil refineries are equipped with mission-critical heavy machinery (boilers, engines, turbines, and so on) and are continuously monitored by thousands of sensors for process efficiency, environmental safety, and predictive maintenance purposes. However, sensors themselves are also prone to errors and failure. The quality of data received from these sensors should be verified before being used in system modeling. There is a need for reliable methods and systems that can provide data validation and reconciliation in real-time with high accuracy. In this paper, we develop a novel method for real-time data validation, gross error detection and classification over multivariate sensor data streams. The validated and high-quality data obtained from these processes is used for pattern analysis and modeling of industrial plants. We obtain sensor data from the power and petrochemical plants of an oil refinery and analyze them using various time-series modeling and data mining techniques that we integrate into a complex event processing engine. Next, we study the computational performance implications of the proposed methods and uncover regimes where they are sustainable over fast streams of sensor data. Finally, we detect shifts among steady-states of data, which represent systems' multiple operating modes and identify the time when a model reconstruction is required using DBSCAN clustering algorithm.Turkish Petroleum Refineries Inc. (TUPRAS) RD CenterPublisher versio

Data stream mining to address big data problems

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Günümüzde bilişim dünyası faydalı bilgiye ulaşma yolunda “büyük veri” problemleri (verinin kütlesi, hızı, çeşitliliği, tutarsızlığı) ile baş etmeye çalışmaktadır. Bu makalede, büyük veri akışları üzerinde İlişkisel Kural Madenciliği’nin (İKM) daha önce literatürde yapılmamış bir şekilde “çevrimiçi” olarak gerçeklenme detayları ile başarım bulguları paylaşılacaktır. Akış madenciliği için Apriori ile FP-Growth algoritmaları Esper isimli olay akış motoruna eklenmiştir. Elde edilen sistem üzerinde bu iki algoritma kayan penceler ve LastFM sosyal müzik sitesi verileri kullanılarak karşılaştırılmıştır. Başarımı yüksek olan FPGrowth seçilerek gerçek-zamanlı ve kural-tabanlı bir tavsiye motoru oluşturulması sağlanmıştır. En önemli bulgularımız çevrimiçi kural çıkarımı sayesinde: (1) çevrimdışı kural çıkarımından çok daha fazla kuralın (2) çok daha hızlı ve etkin olarak ve (3) çok daha önceden hesaplanabileceği gösterilmiştir. Ayrıca müzik zevklerine uygun “George Harrison⇒The Beatles” gibi pekçok ilginç ve gerçekçi kural bulunmuştur. Sonuçlarımızın ileride diğer büyük veri analitik sistemlerinin tasarım ve gerçeklemesine ışık tutacağını ummaktayız.TÜBİTAK ; European Commissio

Processing nested complex sequence pattern queries over event streams

Complex event processing (CEP) has become increasingly important for tracking and monitoring applications ranging from healthcare, supply chain management to surveillance. These monitoring applications submit complex event queries to track sequences of events that match a given pattern. As these systems mature the needfor increasingly complex nested sequence queries arises, while thestate-of-the-art CEP systems mostly focus on the execution of flat sequence queries only. In this paper, we now introduce an iterative execution strategy for nested CEP queries composed of sequence, negation, AND and OR operators. Lastly the promise of applying selective caching of intermediate results to optimize the execution. Our experimental study using real-world stock trades evaluates the performance of our proposed iterative execution strategy for differentquery types.HP Labs Innovation Research Program ; National Science Foundation ; TÜBİTAKpost-prin

Processing nested complex sequence pattern queries over event streams

Complex event processing (CEP) has become increasingly important for tracking and monitoring applications ranging from healthcare, supply chain management to surveillance. These monitoring applications submit complex event queries to track sequences of events that match a given pattern. As these systems mature the needfor increasingly complex nested sequence queries arises, while thestate-of-the-art CEP systems mostly focus on the execution of flat sequence queries only. In this paper, we now introduce an iterative execution strategy for nested CEP queries composed of sequence, negation, AND and OR operators. Lastly the promise of applying selective caching of intermediate results to optimize the execution. Our experimental study using real-world stock trades evaluates the performance of our proposed iterative execution strategy for differentquery types.HP Labs Innovation Research Program ; National Science Foundation ; TÜBİTAKpost-prin

Democratization of runtime verification for internet of things

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Internet of Things (IoT) devices have gained more prevalence in ambient assisted living (AAL) systems. Reliability of AAL systems is critical especially in assuring the safety and well-being of elderly people. Runtime verification (RV) is described as checking whether the observed behavior of a system conforms to its expected behavior. RV techniques generally involve heavy formal methods; thus, it is poorly utilized in the industry. Therefore, we propose a democratization of RV for IoT systems by presenting a model-based testing (MBT) approach. To enable modeling expected behaviors of an IoT system, we first describe an extension to a UML profile. Then, we capture the expected behavior of an interaction that is modeled on a Sequence Diagram (SD). Later, the expected behaviors are translated into runtime monitor statements expressed in Event-Processing Language (EPL), which are executed at the edge of the IoT network. We further demonstrate our contributions on a sample AAL system

Smart job scheduling for high-performance cloud computing services

Due to copyright restrictions, the access to the full text of this article is only available via subscription.In this paper, we describe the challenges faced and lessons learned while establishing a large-scale high performance cloud computing service that enables online mechanical structural analysis and many other scientific applications using the finite element analysis (FEA) technique. The service is intended to process many independent and loosely-dependent (e.g. assembled system) tasks concurrently. Challenges faced include accurate job characterization, handling of many-task mixed jobs, sensitivity of task execution to multi-threading parameters, effective multi-core scheduling in a single node, and achieving seamless scale across multiple nodes. We find that significant performance gains in terms of both job completion latency and throughput are possible via dynamic or "smart" partitioning and resource-aware scheduling compared to shortest first and aggressive job scheduling techniques. We also discuss issues related to secure and private processing of sensitive models in the cloud

Online association rule mining over fast data

Due to copyright restrictions, the access to the full text of this article is only available via subscription.To extract useful and actionable information in real-time, the information technology (IT) world is coping with big data problems today. In this paper, we present implementation details and performance results of ReCEPtor, our system for "online" Association Rule Mining (ARM) over big and fast data streams. Specifically, we added Apriori and two different FP-Growth algorithms inside Esper Complex Event Processing (CEP) engine and compared their performances using LastFM social music site data. Our most important findings show that online ARM can generate (1) more unique rules, (2) with higher throughput, and (3) much sooner (lower latency) than offline rule mining. In addition, we have found many interesting and realistic musical preference rules such as "George HarrisonàBeatles". We demonstrate a sustained rate of ~15K rows/sec per core. We hope that our findings can shed light on the design and implementation of other fast data analytics systems in the future.European Union ; TÜBİTAK ; IBM Shared University Research program ; Turkish Telecomm ; Avea Lab