Conditioning and aggregating uncertain data streams

Tran, Thanh T. L.; McGregor, Andrew; Diao, Yanlei; Peng, Liping; Liu, Anna

doi:10.14778/1920841.1921001

Cited by 19 publications

(10 citation statements)

References 18 publications

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“…Our study is also related to aggregation queries in probabilistic data, e.g., [14], [15], [22], [26], [30], [32]. However, monitoring both score and probability thresholds on aggregate constraints continuously over distributed probabilistic data is clearly different from these studies.…”

Section: Related Workmentioning

confidence: 98%

Efficient Threshold Monitoring for Distributed Probabilistic Data

Tang

Phillips

et al. 2012

2012 IEEE 28th International Conference on Data Engineering

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Abstract-In distributed data management, a primary concern is monitoring the distributed data and generating an alarm when a user specified constraint is violated. A particular useful instance is the threshold based constraint, which is commonly known as the distributed threshold monitoring problem . This work extends this useful and fundamental study to distributed probabilistic data that emerge in a lot of applications, where uncertainty naturally exists when massive amounts of data are produced at multiple sources in distributed, networked locations. Examples include distributed observing stations, large sensor fields, geographically separate scientific institutes/units and many more. When dealing with probabilistic data, there are two thresholds involved, the score and the probability thresholds. One must monitor both simultaneously, as such, techniques developed for deterministic data are no longer directly applicable. This work presents a comprehensive study to this problem. Our algorithms have significantly outperformed the baseline method in terms of both the communication cost (number of messages and bytes) and the running time, as shown by an extensive experimental evaluation using several, real large datasets.

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Section: Related Workmentioning

confidence: 98%

Efficient Threshold Monitoring for Distributed Probabilistic Data

Tang

Phillips

et al. 2012

2012 IEEE 28th International Conference on Data Engineering

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“…Uncertain data streams also have attracted researchers recently. For instance, Tran et al [38] discuss conditioning and aggregation operations on uncertain data streams. Furthermore, some studies consider high-volume uncertain streams specifically.…”

Section: Data Uncertaintymentioning

confidence: 99%

A User-Oriented Taxi Ridesharing System with Large-Scale Urban GPS Sensor Data

Zhang

Shemshadi²,

Sheng

et al. 2021

IEEE Trans. Big Data

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Ridesharing is a challenging topic in the urban computing paradigm, which utilizes urban sensors to generate a wealth of benefits and thus is an important branch in ubiquitous computing. Traditionally, ridesharing is achieved by mainly considering the received user ridesharing requests and then returns solutions to users. However, there lack research efforts of examining user acceptance to the proposed solutions. To our knowledge, user decisions in accepting/rejecting a rideshare is one of the crucial, yet not well studied, factors in the context of dynamic ridesharing. Moreover, existing research attention is mainly paid to find the nearest taxi, whilst in reality the nearest taxi may not be the optimal answer. In this paper, we tackle the above un-addressed issues while preserving the scalability of the system. We present a scalable framework, namely TRIPS, which supports the probability of accepting each request by the companion passengers and minimizes users' efforts. In TRIPS, we propose three search techniques to increase the efficiency of the proposed ridesharing service. We also reformulate the criteria for searching and ranking ridesharing alternatives and propose indexing techniques to optimize the process. Our approach is validated using a real, large-scale dataset of 10,357 GPS-equipped taxis in the city of Beijing, China and showcases its effectiveness on the ridesharing task.

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“…In order to perform the analysis for each of the n transponders placed randomly in selected points P n ( x n ; y n ; z n ) of Ω ID , the Monte Carlo method [99] is used. This technique is quite often applied to solve research problems in the RFID technology, for example in the field of object localization [100], optimization of communication protocols [101], signal detection, collision elimination in multiple systems [102], etc.…”

Section: Determination Of Interrogation Zones In Practical Implemmentioning

confidence: 99%

Factors Affecting the Synthesis of Autonomous Sensors with RFID Interface

Węglarski

Jankowski-Mihułowicz

2019

Sensors

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A general view on the problem of designing atypical battery-free, autonomous semi-passive RFID transponders-sensors (autonomous sensors with RFID interfaces) is presented in this review. Although RFID devices can be created in any of the electronic technologies, the design stage must be repeated each time when the manufacturing processes are changed, and their specific conditions have to be taken into consideration when modeling new solutions. Aspects related to the factors affecting the synthesis of semi-passive RFID transponder components on the basis of which the idea of the autonomous RFID sensor was developed are reflected in the paper. Besides their general characteristics, the operation conditions of modern RFID systems and achievements in autonomous RFID sensor technology are revealed in subsequent sections—they include such issues as technological aspects of the synthesis process, designing antennas for RFID transponders, determining RFID chip and antenna parameters, creating the interrogation zone IZ, etc. It should be pointed that the universal construction of an autonomous RFID sensor, which could be use in any application of the automatic object identification system, cannot be developed according to the current state of the art. Moreover, a trial and error method is the most commonly used in the today’s process of designing new solutions, and the basic parameters are estimated on the basis of the tests and the research team experience. Therefore, it is necessary to look for new inventions and methods in order to improve implementations of RFID systems.

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Conditioning and aggregating uncertain data streams

Cited by 19 publications

References 18 publications

Efficient Threshold Monitoring for Distributed Probabilistic Data

Efficient Threshold Monitoring for Distributed Probabilistic Data

A User-Oriented Taxi Ridesharing System with Large-Scale Urban GPS Sensor Data

Factors Affecting the Synthesis of Autonomous Sensors with RFID Interface

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