Semantic Characterization of Real World Events

Nagargadde, A.; Sridhar, Varadharajan; Ramamritham, Krithi

doi:10.1007/11408079_62

Cited by 9 publications

(10 citation statements)

References 9 publications

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“…With respect to research related to real-world event handling, from [9], we borrowed the idea of representing complex RFID events with the help of state diagrams.…”

Section: Related Workmentioning

confidence: 99%

“…A composite event is a combination of a number of such primitive events linked by predefined operators (e.g., AND, OR, NOT etc.) [9]. In addition to this classification, in an RFID system, we define two types of events -Basic Events and Events of Interest.…”

Section: Events In Rfid Systemsmentioning

confidence: 99%

“…an individual scan of RFID tag affixed to an object in the system. Following [9] and [7] a basic event(b e ) can be defined as a tuple (L, S, T ) where L is the label dimension containing details of the event, S is the location dimension of the event occurrence and T is the time dimension at which the event occurs. As for example, going back to our example of MG's supply chain system, consider the following situation: An object o is being loaded on a truck in a warehouse at location s at a certain point of time t. This object gets scanned by different readers.…”

Section: Basic Eventsmentioning

confidence: 99%

“…Following ECA model [8], a composite EI has three parts (i) event definition (ii) condition and (iii) action to be executed. [9]. Note that, the event definition of a composite EI can be expressed as a regular expression of multiple EIs.…”

Section: Events Of Interest (Ei)mentioning

confidence: 99%

“…Note that, the event definition of a composite EI can be expressed as a regular expression of multiple EIs. Thus the event definition of a composite EI can be represented as a state graph (DFA for regular expressions) as shown in [9]. We will use this event state graph of an EI in section 4.1.…”

Section: Events Of Interest (Ei)mentioning

confidence: 99%

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Real-Time Event Handling in an RFID Middleware System

Dutta

Ramamritham

Karthik

et al.

Databases in Networked Information Systems

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Abstract. Radio Frequency IDentification (RFID) tags have emerged as a key technology for real-time asset tracking. Wide application of RFID leads to huge amounts of data being generated from scan of each of these RFID tags on individual items, for example, the RFID system of a moderate size retail chain will generate 300 million RFID scans per day. Extracting meaningful information out of this huge amount of scan data is a challenging task. Moreover CIOs are looking for real time business decision from this RFID scan data. In this paper we show how to add value to an RFID middleware system by enabling it to handle a large number of RFID scan data and execute business rules in real-time. Experimentally we demonstrate that our proposed approach is very time efficient compare to a similar implementation with existing technologies. Lastly we also propose an architecture for a distributed RFID middleware system to handle raw RFID scan data.

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“…With respect to research related to real-world event handling, from [9], we borrowed the idea of representing complex RFID events with the help of state diagrams.…”

Section: Related Workmentioning

confidence: 99%

Section: Events In Rfid Systemsmentioning

confidence: 99%

Section: Basic Eventsmentioning

confidence: 99%

Section: Events Of Interest (Ei)mentioning

confidence: 99%

Section: Events Of Interest (Ei)mentioning

confidence: 99%

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Real-Time Event Handling in an RFID Middleware System

Dutta

Ramamritham

Karthik

et al.

Databases in Networked Information Systems

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Event Specification and Processing for Advanced Applications: Generalization and Formalization

Adaikkalavan

Chakravarthy

Lecture Notes in Computer Science

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Managing RFID events in large-scale distributed RFID infrastructures

2011

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As RFID installations become larger and more geographically distributed, their scalability becomes a concern. Currently, most RFID processing occurs in a central location, gathering tag scans and matching them to event-condition-action (ECA) rules. However, as the number of scans and ECA rules grows, the workload quickly outpaces the capacity of a centralized processing server. In this paper, we consider the problem of distributing the RFID processing workload across multiple nodes in the system. We describe the problem, and present an overview of our approach. We then formulate two decision models for distributing the processing across the system. One generates an optimal allocation based on global awareness of the state of the system. This problem is NP-hard and assumes that bandwidth and processing resource availability is known in a central location, which is unrealistic in real scenarios. Thus, we use this model as a theoretical optimal model for comparison purposes. The second model generates a set of local decisions based on locally-available processing and bandwidth information, which takes much less information into account than the global model, but still produces useful results. We describe our system architecture, and present a set of experimental results that demonstrate that (a) the global model, while providing an optimal allocation of processing responsibilities, model does not scale well, requiring hours to solve problems that the localized model can solve in a few tens of seconds; (b) the localized model generates usable solutions, differing from the optimal solution on average by 2.1% for smaller problem sizes and at most 5.8% in the largest problem size compared; and (c) the localized approach can provide runtime performance near that of the global model, within 3-5% of the global model, and up to a 55% improvement in runtime performance over a (uniform) random allocation.

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Semantic Characterization of Real World Events

Cited by 9 publications

References 9 publications

Real-Time Event Handling in an RFID Middleware System

Real-Time Event Handling in an RFID Middleware System

Event Specification and Processing for Advanced Applications: Generalization and Formalization

Managing RFID events in large-scale distributed RFID infrastructures

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