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Temporal Alignment Model for Data Streams in Wireless Sensor Networks Based on Causal Dependencies

Jose Roberto Perez Cruz, Saul E. Pomares Hernandez
2014 International Journal of Distributed Sensor Networks  
Specifically, in a WSN this paradigm of data generation/transmission is known as event-streaming.  ...  In this paper, we propose a new model called Event-Streaming Logical Mapping (ES-LM), for the temporal data alignment in WSNs.  ...  Intervals. An interval is a set of events which occur during a period of time. If the events that compose an interval satisfy a certain order, then such interval is called ordered interval.  ... 
doi:10.1155/2014/938698 fatcat:wfladb7stnfy3ntn3pl55tvuxi

A framework for event co-occurrence detection in event streams [article]

Laleh Jalali, Ramesh Jain
2016 arXiv   pre-print
Finally a processing algorithm is applied to count the occurrence frequency of a collection of patterns with only one pass through input event streams.  ...  The method proposed in this paper can be used for detecting co-occurrences between both events of one event stream (Auto co-occurrence), and events from multiple event streams (Cross co-occurrence).  ...  When processing the i th event in the serialized event stream, the automata in waits(Ei) are considered.  ... 
arXiv:1603.09012v1 fatcat:nzw34pesmveetcmnat5ycqeote

Performance Analysis of Multimedia Applications using Correlated Streams

Kai Huang, Lothar Thiele, Todor Stefanov, Ed Deprettere
2007 2007 Design, Automation & Test in Europe Conference & Exhibition  
In modern embedded systems, data streams are often partitioned into separate sub-streams which are processed on parallel hardware components.  ...  To analyze the performance of these systems with high accuracy, correlations between event streams must be taken into account.  ...  The split process selects events that are transferred to stream 2 and then appear without any delay at the output stream. α ′ u (∆) is the maximum number of events in some interval of length ∆ and let  ... 
doi:10.1109/date.2007.364409 dblp:conf/date/HuangT07 fatcat:6t42i2j36zfa3nhea4uuxg4y4a

Toward multimodal situated analysis

Chreston Miller, Francis Quek
2011 Proceedings of the 13th international conference on multimodal interfaces - ICMI '11  
We process events on a semi-interval level to provide detailed temporal ordering of events with respect to instances of a phenomenon.  ...  In this paper, we propose an automatic processing approach that supports this need for situated analysis in multimodal data.  ...  Our first step is to convert these data streams into a homogeneous stream of events. An event is an interval in time representing when an action begins and ends.  ... 
doi:10.1145/2070481.2070526 dblp:conf/icmi/MillerQ11 fatcat:nlwpuuzb4be7vjjrwwqe6n3vs4

Bringing Deep Causality to Multimedia Data Streams

Laleh Jalali, Ramesh Jain
2015 Proceedings of the 23rd ACM international conference on Multimedia - MM '15  
Causal modeling across multimedia data streams is essential to reap the potential of this data.  ...  We show the applicability of the framework in a an important Asthma application using heterogeneous data streams.  ...  Finally a processing algorithm is used to find instances of that pattern in the input event streams.  ... 
doi:10.1145/2733373.2806278 dblp:conf/mm/JalaliJ15 fatcat:bnbkmwdqyvbrlpiuakncmfnh7q

An Efficient Approach for Mining Segment-Wise Intervention Rules in Time-Series Streams [chapter]

Yue Wang, Jie Zuo, Ning Yang, Lei Duan, Hong-Jun Li, Jun Zhu
2010 Lecture Notes in Computer Science  
Huge time-series stream data are collected every day from many areas, and their trends may be impacted by outside events, hence biased from its normal behavior.  ...  To solve these challenges, this study makes the following contributions: (a) Proposes a framework to detect intervention events in time-series streams, (b) Proposes approaches to evaluate the impact of  ...  Hidden Markov Model for Stream Processing System. Consider a stream processing system.  ... 
doi:10.1007/978-3-642-14246-8_25 fatcat:c6tw274ydballba6zkzmymuq7i

Efficient Data Processing for Large-Scale Cloud Services

Marcel Tilly, Stephan Reiff-Marganiec, Helge Janicke
2012 2012 IEEE Eighth World Congress on Services  
techniques adopted from complex event processing.  ...  one side and data consumers on the other side, such as in the Internet of Things, large scale sensor networks, machine to machine communication or even in social media, one emerging requirement is to process  ...  Conceptually the service is filtering its event stream as depicted in Figure 8 . Here the Service is processing its own stream of events (Service Events) that define its internal behaviour.  ... 
doi:10.1109/services.2012.41 dblp:conf/services/TillyRJ12 fatcat:eerhzrz76regplrs2dm2i5vqnu

Probabilistic Timing Join over Uncertain Event Streams

Aloysius K. Mok, Honguk Woo, Chan-Gun Lee
2006 12th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA'06)  
This paper addresses the problem of processing eventtiming queries over event streams where the uncertainty in the values of the timestamps is characterizable by histograms.  ...  We describe a stream-partitioning technique for checking the satisfaction of a probabilistic timing constraint upon event arrivals in a systematic way in order to delimit the "probing range" in event streams  ...  at which the events are detected from processing the data streams. This mode of query processing is said to be by detection semantics.  ... 
doi:10.1109/rtcsa.2006.52 dblp:conf/rtcsa/MokWL06 fatcat:vrqhiz6k5zcwja5i44uoo3zj7e

Heuristic Event Filtering Methodology for Interval based Temporal Semantics

V. Govindasamy, P. Thambidurai
2013 International Journal of Computer Applications  
Eno t+ SAMPLER INSTANCE STACK COMPLEX EVENT PROCESSING ENGINE PATTERNS DETECTED Event Stream 1 Event Stream 2 Event Stream n . . .  ...  The size of the Sliding Window depends on the time interval. The sampler component in the filter partitions the Sliding Window. An event record from each partition is processed.  ... 
doi:10.5120/11974-7836 fatcat:te5ecf4dazbj5odl2sabiqguja

Self-adaptive event recognition for intelligent transport management

Alexander Artikis, Matthias Weidlich, Avigdor Gal, Vana Kalogeraki, Dimitrios Gunopulos
2013 2013 IEEE International Conference on Big Data  
We report on the use of a logic-based event reasoning tool to identify regions of uncertainty within a stream and demonstrate our method with a real-world use-case from the city of Dublin.  ...  In this work we tackle the issue of uncertainty in transportation systems stream reporting.  ...  Event processing platforms are diversified into products with various approaches towards event processing, including the stream oriented approach, the rule oriented approach, the imperative approach, and  ... 
doi:10.1109/bigdata.2013.6691590 dblp:conf/bigdataconf/ArtikisWGKG13 fatcat:mkxp3c7drbdlfesxilsomy5stm

Significance-Based Failure and Interference Detection in Data Streams [chapter]

Nickolas J. G. Falkner, Quan Z. Sheng
2009 Lecture Notes in Computer Science  
In this paper, we model the traffic in data streams as a set of significant events, with an arrival rate distributed with a Poisson distribution.  ...  Detecting the failure of a data stream is relatively easy when the stream is continually full of data.  ...  Several challenges occur when processing a data stream.  ... 
doi:10.1007/978-3-642-03573-9_54 fatcat:v4us6chpzvhbtpscpf77gfrikq

Being Logical or Going with the Flow? A Comparison of Complex Event Processing Systems [chapter]

Elias Alevizos, Alexander Artikis
2014 Lecture Notes in Computer Science  
Complex event processing (CEP) is a field that has drawn significant attention in the last years.  ...  CEP systems treat incoming information as flows of time-stamped events which may be structured according to some underlying pattern.  ...  to the problem of stream processing and pattern matching.  ... 
doi:10.1007/978-3-319-07064-3_40 fatcat:xynicmscfrc6vngzjlp55wei24

Semantics-Based Complex Event Processing for RFID Data Streams

Tao Ku, YunLong Zhu, KunYuan Hu
2007 The First International Symposium on Data, Privacy, and E-Commerce (ISDPE 2007)  
In this paper we present the semantics-based CEP (Complex Event Processing) infrastructure on hierarchical data model for the capture, filtering and automatic transformation RFID data, and provide complex  ...  event detection patterns and algorithms for the generation of business logic semantic information.  ...  However, CEP [2] engines can process streaming data with the goal of identifying the meaningful events within those streams with in real time, which consume multiple streams of event-oriented data, analyze  ... 
doi:10.1109/isdpe.2007.9 fatcat:wnwneepjxnf2pmizy66zdnil5m

Research on Complex Event Detection over Streams Supporting Interval Temporal Logic and Regular Expression Pattern Matching

Jia-wei ZHANG, Hu-sheng LIAO, Hong-yu GAO, Chang SU
2018 DEStech Transactions on Engineering and Technology Research  
To improve the detective ability of complex event processing (CEP), we combine interval temporal logic (ITL) and regular expression in pattern description of event streams constraint.  ...  However, most complex event processing system focus on discovering the point-Based events pattern, but they have insufficient support for interval-based events pattern detect.  ...  Experimental Setup A four-node cluster environment with one node for event detection agents, one node for CEP server, and two nodes for stream data processing.  ... 
doi:10.12783/dtetr/icmeit2018/23460 fatcat:7etousk4cjh4xlvsde7w6uggai

Low-Latency Service Data Aggregation Using Policy Obligations

Stephan Reiff-Marganiec, Marcel Tilly, Helge Janicke
2014 2014 IEEE International Conference on Web Services  
This work is introducing new concepts for enabling fast communication by limiting information flow through filtering concepts combined with data processing techniques adopted from complex event processing  ...  The filter policies describe temporal conditions between two events removing the need to keep a complete history while still allowing temporal reasoning.  ...  Conceptually the service is filtering its event stream as depicted in Figure 4 . Here the Service is processing its own stream of events (Service Events) that define its internal behaviour.  ... 
doi:10.1109/icws.2014.80 dblp:conf/icws/Reiff-MarganiecTJ14 fatcat:7mj75t6b3vajdfelp5xja5pevu
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