Situation-Aware Adaptive Processing (SAAP) of Data Streams

Haghighi, Pari Delir; Gaber, Mohamed Medhat; Krishnaswamy, Shonali; Zaslavsky, Arkady

doi:10.1007/978-1-84882-599-4_14

Cited by 3 publications

(5 citation statements)

References 9 publications

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“…The other concept which is more global than context is situation. This difference is illustrated by D. Haghighi [14] in an example: -in a smart room scenario, rather than monitoring sensed context from light, noise and motion sensors individually, this information can be used to reason about situations, such as -meeting,‖ -presentation‖ or -study,‖ which provides a better understanding of the environment‖. The notion of a situation is then defined as being a meta-level concept over context, which is inferred from contextual information.…”

Section: Related Workmentioning

confidence: 99%

RFID-driven situation awareness on TangiSense, a table interacting with tangible objects

Kubicki

Lepreux

Kolski

2011

Pers Ubiquit Comput

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International audienceInteractive tables are increasingly present not only in a research context but also in everyday life. One challenge still has to be taken up concerning interaction with several people around an interactive table. The framework of our research relates to an interactive table named TangiSense, which is equipped with RFID technology. The interaction is no longer the result of touching the table, but comes from handling tangible objects placed on the table. The use is closer to the natural use of a table. This technology makes it possible to identify the objects, which can be coupled with the users. Starting from this principle, the article proposes the use of RFID tags to collect the elements of context awareness in order to adapt the workspaces to the various possible situations around a table (work alone or with several users, on a common or individual space). An algorithm of space division is proposed, and evaluations are carried out in order to assess its global contribution. The article ends with a conclusion and some prospects

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Section: Related Workmentioning

confidence: 99%

RFID-driven situation awareness on TangiSense, a table interacting with tangible objects

Kubicki

Lepreux

Kolski

2011

Pers Ubiquit Comput

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“…In addition to availability of resources, mobile data mining application's accuracy requirements vary according to the occurring situations. A situation‐aware adaptation technique controls the data stream mining settings according to the occurring situations and accuracy requirements of the running application . There can be other scenarios in which it is important to adjust mining algorithms considering both, the current situation and resource availability in order to maintain the application's accuracy needs as well as the continuity of mining operations.…”

Section: Ubiquitous Data Mining: From Algorithms To Applicationsmentioning

confidence: 99%

“…There can be other scenarios in which it is important to adjust mining algorithms considering both, the current situation and resource availability in order to maintain the application's accuracy needs as well as the continuity of mining operations. In such cases, there is a need to apply a hybrid adaptation strategy that combines situation and resource‐aware adaptation methods …”

Section: Ubiquitous Data Mining: From Algorithms To Applicationsmentioning

confidence: 99%

“…Using criticality values and thresholds enables the adaptation process to compare resources according to their levels and situations with regard to the application's accuracy requirement, and determine which strategy can achieve the required accuracy while using resources efficiently. We refer the readers to Ref for a formal and detailed specification of situation‐aware adaptation for mobile or ubiquitous data stream mining.…”

Section: Ubiquitous Data Mining: From Algorithms To Applicationsmentioning

confidence: 99%

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Data stream mining in ubiquitous environments: state‐of‐the‐art and current directions

Gaber

Gama

Krishnaswamy

et al. 2014

WIREs Data Min & Knowl

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In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. activity recognition, smart homes, and emergency or disaster management like bush fires.1,2 Thus, the key focus is on developing data stream mining algorithms that are highly scalable or computationally efficient, energy-efficient, and context or resource-aware. These features enable the continued operation of data stream mining algorithms in a highly dynamic mobile or ubiquitous environment. The typical constraints that have to be addressed in performing mobile or ubiquitous data stream mining are: (1) data streams are generated and sent in real time in a stream format with little or no potential for persistent storage, (2) resource constraints include limited computational resources such as memory, processor speed, network bandwidth, battery power, and screen real-estate, (3) temporal constraints refer to real-time information and decision-making needs that, in turn, necessitate the analysis to be online, incremental, and continuous, (4) mobility of users and devices and the connectivity issues thereof, and 116

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“…For example, a health monitoring application requires lower accuracy when the patient is healthy and the occurring situation is 'normal'. A situation-aware adaptation technique controls the data stream mining settings according to current situations and accuracy requirements to improve the continuity of the running application (Delir Haghighi et al 2010). There can be other scenarios in which it is important to adjust mining algorithms considering both the current situation and resource…”

Section: Introductionmentioning

confidence: 99%

Open Mobile Miner: A Toolkit for Building Situation-Aware Data Mining Applications

Haghighi

Krishnaswamy

Zaslavsky

et al. 2013

Journal of Organizational Computing and Electronic Commerce

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In organizational computing and information systems, data mining techniques have been widely used for analyzing customer behaviour and discovering hidden patterns. Mobile Data Mining is the process of intelligently analysing continuous data streams on mobile devices. The use of mobile data mining for realtime business intelligence applications can be greatly advantageous. Past research has shown that resource-aware adaptation of data stream mining can significantly improve the continuity of data mining operations in mobile environments. The key underlying premise is that by varying the accuracy of the analysis process in accordance with changing available resource levels, the longevity and continuity of mobile data mining applications is ensured. In this paper we qualitatively extend the notion of resourceaware adaptation of mobile data mining to holistically enable situation-awareness feature for user applications. We then present a novel generic toolkit that enables building situation and resource-aware mobile data mining applications and describe along with underlying theoretical foundations of resource and situation criticality, awareness and adaptation which are entirely transparent and hidden from the user. The Open Mobile Miner (OMM) toolkit builds on our research for performing adaptive analysis of data streams on mobile/embedded devices. Finally, we describe a mobile health monitoring application as a case study and discuss the results of our conducted experimental evaluation which demonstrate the adaptation transparency and easy use of OMM for building mobile data mining applications such as stock market monitoring and real estate data analysis.

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Situation-Aware Adaptive Processing (SAAP) of Data Streams

Cited by 3 publications

References 9 publications

RFID-driven situation awareness on TangiSense, a table interacting with tangible objects

RFID-driven situation awareness on TangiSense, a table interacting with tangible objects

Data stream mining in ubiquitous environments: state‐of‐the‐art and current directions

Open Mobile Miner: A Toolkit for Building Situation-Aware Data Mining Applications

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