Capturing Data Uncertainty in High-Volume Stream Processing

We present the design and development of a data stream system that captures data uncertainty from data collection to query processing to final result generation. Our system focuses on data that is naturally modeled as continuous random variables. For such data, our system employs an approach grounded in probability and statistical theory to capture data uncertainty and integrates this approach into high-volume stream processing. The first component of our system captures uncertainty of raw data streams from sensing devices. Since such raw streams can be highly noisy and may not carry sufficient information for query processing, our system employs probabilistic models of the data generation process and stream-speed inference to transform raw data into a desired format with an uncertainty metric. The second component captures uncertainty as data propagates through query operators. To efficiently quantify result uncertainty of a query operator, we explore a variety of techniques based on probability and statistical theory to compute the result distribution at stream speed. We are currently working with a group of scientists to evaluate our system using traces collected from the domains of (and eventually in the real systems for) hazardous weather monitoring and object tracking and monitoring.Comment: CIDR 200

A framework for realistic 3D tele-immersion

Meeting, socializing and conversing online with a group of people using teleconferencing systems is still quite differ- ent from the experience of meeting face to face. We are abruptly aware that we are online and that the people we are engaging with are not in close proximity. Analogous to how talking on the telephone does not replicate the experi- ence of talking in person. Several causes for these differences have been identified and we propose inspiring and innova- tive solutions to these hurdles in attempt to provide a more realistic, believable and engaging online conversational expe- rience. We present the distributed and scalable framework REVERIE that provides a balanced mix of these solutions. Applications build on top of the REVERIE framework will be able to provide interactive, immersive, photo-realistic ex- periences to a multitude of users that for them will feel much more similar to having face to face meetings than the expe- rience offered by conventional teleconferencing systems

Process intensification for post combustion CO₂ capture with chemical absorption: a critical review

The concentration of CO₂ in the atmosphere is increasing rapidly. CO₂ emissions may have an impact on global climate change. Effective CO₂ emission abatement strategies such as carbon capture and storage (CCS) are required to combat this trend. Compared with pre-combustion carbon capture and oxy-fuel carbon capture approaches, post-combustion CO₂ capture (PCC) using solvent process is one of the most mature carbon capture technologies. There are two main barriers for the PCC process using solvent to be commercially deployed: (a) high capital cost; (b) high thermal efficiency penalty due to solvent regeneration. Applying process intensification (PI) technology into PCC with solvent process has the potential to significantly reduce capital costs compared with conventional technology using packed columns. This paper intends to evaluate different PI technologies for their suitability in PCC process. The study shows that rotating packed bed (RPB) absorber/stripper has attracted much interest due to its high mass transfer capability. Currently experimental studies on CO₂ capture using RPB are based on standalone absorber or stripper. Therefore a schematic process flow diagram of intensified PCC process is proposed so as to motivate other researches for possible optimal design, operation and control. To intensify heat transfer in reboiler, spinning disc technology is recommended. To replace cross heat exchanger in conventional PCC (with packed column) process, printed circuit heat exchanger will be preferred. Solvent selection for conventional PCC process has been studied extensively. However, it needs more studies for solvent selection in intensified PCC process. The authors also predicted research challenges in intensified PCC process and potential new breakthrough from different aspects

Combining Stream Mining and Neural Networks for Short Term Delay Prediction

The systems monitoring the location of public transport vehicles rely on wireless transmission. The location readings from GPS-based devices are received with some latency caused by periodical data transmission and temporal problems preventing data transmission. This negatively affects identification of delayed vehicles. The primary objective of the work is to propose short term hybrid delay prediction method. The method relies on adaptive selection of Hoeffding trees, being stream classification technique and multilayer perceptrons. In this way, the hybrid method proposed in this study provides anytime predictions and eliminates the need to collect extensive training data before any predictions can be made. Moreover, the use of neural networks increases the accuracy of the predictions compared with the use of Hoeffding trees only