Pari Delir Haghighi
Monash University
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Publication
Featured researches published by Pari Delir Haghighi.
european conference on smart sensing and context | 2008
Pari Delir Haghighi; Shonali Krishnaswamy; Arkady B. Zaslavsky; Mohamed Medhat Gaber
Context-awareness is a key to enabling intelligent adaptation in pervasive computing applications that need to cope with dynamic and uncertain environments. Addressing uncertainty is one of the major issues in context-based situation modeling and reasoning approaches. Uncertainty can be caused by inaccuracy, ambiguity or incompleteness of sensed context. However, there is another aspect of uncertainty that is associated with human concepts and real-world situations. In this paper we propose and validate a Fuzzy Situation Inference (FSI) technique that is able to represent uncertain situations and reflect delta changes of context in the situation inference results. The FSI model integrates fuzzy logic principles into the Context Spaces (CS) model, a formal and general context reasoning and modeling technique for pervasive computing environments. The strengths of fuzzy logic for modeling and reasoning of imperfect context and vague situations are combined with the CS models underlying theoretical basis for supporting context-aware pervasive computing scenarios. An implementation and evaluation of the FSI model are presented to highlight the benefits of the FSI technique for context reasoning under uncertainty.
Journal of Organizational Computing and Electronic Commerce | 2013
Pari Delir Haghighi; Shonali Krishnaswamy; Arkady B. Zaslavsky; Mohamed Medhat Gaber; Abhijat Sinha; Brett Gillick
In organizational computing and information systems, data mining techniques have been widely used for analyzing customer behavior and discovering hidden patterns. Mobile Data Mining is the process of intelligently analyzing continuous data streams on mobile devices. The use of mobile data mining for real-time 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 article we qualitatively extend the notion of resource-aware 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.
intelligent data analysis | 2009
Pari Delir Haghighi; Arkady B. Zaslavsky; Shonali Krishnaswamy; Mohamed Medhat Gaber; Seng Wai Loke
In resource-constrained devices, adaptation of data stream processing to variations of data rates and availability of resources is crucial for consistency and continuity of running applications. However, to enhance and maximize the benefits of adaptation, there is a need to go beyond mere computational and device capabilities to encompass the full spectrum of context-awareness. This paper presents a general approach for context-aware adaptive mining of data streams that aims to dynamically and autonomously adjust data stream mining parameters according to changes in context and situations. We perform intelligent and real-time analysis of data streams generated from sensors that is under-pinned using context-aware adaptation. A prototype of the proposed architecture is implemented and evaluated in the paper through a real-world scenario in the area of healthcare monitoring.
hawaii international conference on system sciences | 2013
Prem Prakash Jayaraman; Kutila Gunasekera; Frada Burstein; Pari Delir Haghighi; Harris Sebastian Soetikno; Arkady B. Zaslavsky
Managing mass gathering events is a complex process. A key factor in successful management of mass gatherings is the availability of real-time data about the situation. This data is very useful in making important decisions that can potentially be life saving. In this paper, we propose an ontology-based framework for real-time collection and visualization of on field mobile triage data. The proposed framework uses a mobile device-assisted triage decision making system (iTriage). The system leverages a Service Oriented Architecture (SOA) to efficiently share and visualize triage data aiding mass gathering administrators in determining the exact state of the situation. Our framework is driven by a domain ontology for mass gathering ensuring consistency in the shared data. We demonstrate a prototype implementation of the iTriage system on android-based mobile devices. Our experimental evaluations validate the iTriage systems ability to accurately compute patient priorities based on the triage sieve algorithm. Further, we demonstrate the capability of the proposed framework to visualize triage data on a map in real time.
decision support systems | 2010
Pari Delir Haghighi; Frada Burstein; Arkady B. Zaslavsky; Paul Arbon; Shonali Krishnaswami
Mass gatherings are common events that typically attract large crowds of people. If such events are not properly planned, coordinated and managed with regard to health and safety issues, they can become hazardous and lead to injuries, illnesses and deaths. Conducting a safe and successful mass gathering event requires effective planning and management including the provision of timely medical care and response. To achieve these goals, there is a need for a unifying and formal model/framework of mass gatherings that can be applied across all the emergency agencies and events, and used in support of time-critical decision making for medical emergency management in this context. However, the absence of a common knowledge structure and conceptual model in Medical Emergency Management in Mass Gathering (MEMMG), acknowledged in the literature, limits our understanding of such events and impedes the effectiveness of decision support systems in these environments. In this paper, we propose domain ontology for MEMMG that represents main concepts of mass gatherings and their characteristics and relationships in a standard and formal manner. The proposed domain ontology is an instantiation and extension of DOEM (Domain Ontology for Emergency Management) that represents major generic concepts in the emergency management and can be used as information structure for the development of various emergency management decision support systems. We illustrate application of such an ontology to the classical Intelligence-Design-Choice-Implementation decision support model.
service-oriented computing and applications | 2010
Pari Delir Haghighi; Hasn Al Taiar; Frada Burstein; Paul Arbon; Shonali Krishnaswamy
To achieve timely response and treatment of life threatening injury or illness in mass gatherings, it is imperative to establish effective communication and coordination between emergency agencies and teams and provide them with ready access to real-time information. The service-oriented paradigm presents an elegant solution for developing a general and scalable architecture where emergency teams can utilize services for situational-awareness and collaboration. In this paper we propose an ontology-based service-oriented architecture for mass gathering health services that can be implemented using stationary servers or peer-to-peer models. We describe the preliminary implementation of the service-oriented system using an Android phone.
JMIR public health and surveillance | 2017
Pari Delir Haghighi; Yong-Bin Kang; Rachelle Buchbinder; Frada Burstein; Samuel L Whittle
Background Little is understood about the determinants of symptom expression in individuals with fibromyalgia syndrome (FMS). While individuals with FMS often report environmental influences, including weather events, on their symptom severity, a consistent effect of specific weather conditions on FMS symptoms has yet to be demonstrated. Content analysis of a large number of messages by individuals with FMS on Twitter can provide valuable insights into variation in the fibromyalgia experience from a first-person perspective. Objective The objective of our study was to use content analysis of tweets to investigate the association between weather conditions and fibromyalgia symptoms among individuals who tweet about fibromyalgia. Our second objective was to gain insight into how Twitter is used as a form of communication and expression by individuals with fibromyalgia and to explore and uncover thematic clusters and communities related to weather. Methods Computerized sentiment analysis was performed to measure the association between negative sentiment scores (indicative of severe symptoms such as pain) and coincident environmental variables. Date, time, and location data for each individual tweet were used to identify corresponding climate data (such as temperature). We used graph analysis to investigate the frequency and distribution of domain-related terms exchanged in Twitter and their association strengths. A community detection algorithm was applied to partition the graph and detect different communities. Results We analyzed 140,432 tweets related to fibromyalgia from 2008 to 2014. There was a very weak positive correlation between humidity and negative sentiment scores (r=.009, P=.001). There was no significant correlation between other environmental variables and negative sentiment scores. The graph analysis showed that “pain” and “chronicpain” were the most frequently used terms. The Louvain method identified 6 communities. Community 1 was related to feelings and symptoms at the time (subjective experience). It also included a list of weather-related terms such as “weather,” “cold,” and “rain.” Conclusions According to our results, a uniform causal effect of weather variation on fibromyalgia symptoms at the group level remains unlikely. Any impact of weather on fibromyalgia symptoms may vary geographically or at an individual level. Future work will further explore geographic variation and interactions focusing on individual pain trajectories over time.
the internet of things | 2016
Alexey Medvedev; Alireza Hassani; Arkady B. Zaslavsky; Prem Prakash Jayaraman; Maria Indrawan-Santiago; Pari Delir Haghighi; Sea Ling
Internet of Things is a very active research area with great commercialisation potential. The number of IoT platforms is already exceeding 300 and still growing. However, performance evaluation and benchmarking of IoT platforms are still in their infancy. As a step towards developing a performance benchmarking approach for IoT platforms, this paper analyses and compares a number of popular IoT platforms from data ingestion and storage capability perspectives. In order to test the proposed approach, we use the widely used open source IoT platform, OpenIoT. The results of the experiments and the lessons learnt are presented and discussed. While having a great research promise and pioneering contribution to semantic interoperability of IoT silos, the experimental results indicate OpenIoT platform needs more development effort to be ready for any substantial deployment in commercial IoT applications.
Journal of Decision Systems | 2013
Pari Delir Haghighi
The technological evolution in mobile computing and communication provides an exciting opportunity for a new class of decision support systems (DSS) known as Mobile Decision Support Systems (MDSS). These systems can be very beneficial to a range of application domains where complex and critical decisions are made under time pressure, decision-makers are on the move, and the environment is dynamic and uncertain. Examples include mobile healthcare, emergency management, mobile policing, mobile commerce and mobile banking. Access to up-to-date information and data on mobile devices can significantly enhance DSS and support mobility of decision-makers by accessing and/or analysing data where and when it is needed the most. While the coming together of technology drivers has enabled the delivery of sophisticated real-time knowledge, the key questions of how this information is incorporated into the DSS and how it is processed to enable real-time decision-making onboard mobile devices have not been fully answered.
international conference on conceptual structures | 2016
Jonathan Samosir; Maria Indrawan-Santiago; Pari Delir Haghighi
Real-time data stream processing technologies play an important role in enabling time-critical decision making in many applications. This paper aims at evaluating the performance of platforms that are capable of processing streaming data. Candidate technologies include Storm, Samza, and Spark Streaming. To form the recommendation, a prototype pipeline is designed and implemented in each of the platforms using data collected from sensors used in monitoring heavy-haul railway systems. Through the testing and evaluation of each candidate platform, using both quantitative and qualitative metrics, the paper describes the findings, where Storm is found to be the most appropriate candidate.
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Commonwealth Scientific and Industrial Research Organisation
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