Supporting Scientific Analytics under Data Uncertainty and Query Uncertainty

Data management is becoming increasingly important in many applications, in particular, in large scientific databases where (1) data can be naturally modeled by continuous random variables, and (2) queries can involve complex predicates and/or be difficult for users to express explicitly. My thesis work aims to provide efficient support to both the data uncertainty and the query uncertainty . When data is uncertain, an important class of queries requires query answers to be returned if their existence probabilities pass a threshold. I start with optimizing such threshold query processing for continuous uncertain data in the relational model by (i) expediting selections by reducing dimensionality of integration and using faster filters, (ii) expediting joins using new indexes on uncertain data, and (iii) optimizing a query plan using a dynamic, per-tuple based approach. Evaluation results using real-world data and benchmark queries show the accuracy and efficiency of my techniques and the dynamic query planning has over 50% performance gains in most cases over a state-of-the-art threshold query optimizer and is very close to the optimal planning in all cases. Next I address uncertain data management in the array model, which has gained popu- larity for scientific data processing recently due to performance benefits. I define the formal semantics of array operations on uncertain data involving both value uncertainty within individual tuples and position uncertainty regarding where a tuple should belong in an array given uncertain dimension attributes, and propose a suite of storage and evaluation strategies for array operators, with a focus on a novel scheme that bounds the overhead of querying by strategically placing a few replicas of the tuples with large variances. Evaluation results show that for common workloads, my best-performing techniques outperform baselines up to 1 to 2 orders of magnitude while incurring only small storage overhead. Finally, to bridge the increasing gap between the fast growth of data and the limited human ability to comprehend data and help the user retrieve high-value content from data more effectively, I propose to build interactive data exploration as a new database service, using an approach called “explore-by-example”. To build an effective system, my work is grounded in a rigorous SVM-based active learning framework and focuses on the following three problems: (i) accuracy-based and convergence-based stopping criteria, (ii) expediting example acquisition in each iteration, and (iii) expediting the final result retrieval. Evaluation results using real-world data and query patterns show that my system significantly outperforms state-of-the-art systems in accuracy (18x accuracy improvement for 4-dimensional workloads) while achieving desired efficiency for interactive exploration (2 to 5 seconds per iteration)

The Application and Research of navigation-aids inspection and maintenance based on video surveillance

AbstractTo realize automatic management of navigation-aids, this paper presented an idea of navigation-aids inspection using video surveillance technology. It was aimed at solving problems of navigation-aids inspection and maintenance mode. It analyzed the current status of navigation-aids inspection and maintenance in areas, and researched the development and application of video surveillance technology. The results indicated that navigation-aids inspection and maintenance with video surveillance can better improve work efficiency, save costs and protect the quality of navigation-aids service

Information retrieval of mass encrypted data over multimedia networking with N-level vector model-based relevancy ranking

With an explosive growth in the deployment of networked applications over the Internet, searching the encrypted information that the user needs becomes increasingly important. However, the information search precision is quite low when using Vector space model for mass information retrieval, because long documents having poor similarity values are poorly represented in the vector space model and the order in which the terms appear in the document is lost in the vector space representation with intuitive weighting. To address the problems, this study proposed an N-level vector model (NVM)-based relevancy ranking scheme with an introduction of a new formula of the term weighting, taking into account the location of the feature term in the document to describe the content of the document properly, investigated into ways of ranking the encrypted documents using the proposed scheme, and conducted realistic simulation of information retrieval of mass encrypted data over multimedia networking. Results indicated that the timing of the index building, the most costing part of the relevancy ranking scheme, increased with the increase in both the document size and the multimedia content of the document being searched, which is in agreement with the expected. Performance evaluation demonstrated that our specially designed NVM-based encrypted information retrieval system is effective in ranking the encrypted documents transmitted over multimedia networks with large recall ratio and great retrieval precision

T cell immune abnormalities in immune thrombocytopenia

Immune thrombocytopenia is an autoimmune disease with abnormal T cell immunity. Cytotoxic T cells, abnormal T regulatory cells, helper T cell imbalance, megakaryocyte maturation abnormalities and abnormal T cell anergy are involved in the pathogenesis of this condition. The loss of T cell-mediated immune tolerance to platelet auto-antigens plays a crucial role in immune thrombocytopenia. The induction of T cell tolerance is an important mechanism by which the pathogenesis and treatment of immune thrombocytopenia can be studied. Studies regarding the roles of the new inducible costimulator signal transduction pathway, the ubiquitin proteasome pathway, and the nuclear factor kappa B signal transduction pathway in the induction of T cell tolerance can help improve our understanding of immune theory and may provide a new theoretical basis for studying the pathogenesis and treatment of immune thrombocytopenia

Global Exponential Stability of Impulsive Functional Differential Equations via Razumikhin Technique

This paper develops some new Razumikhin-type theorems on global exponential stability of impulsive functional differential equations. Some applications are given to impulsive delay differential equations. Compared with some existing works, a distinctive feature of this paper is to address exponential stability problems for any finite delay. It is shown that the functional differential equations can be globally exponentially stabilized by impulses even if it may be unstable itself. Two examples verify the effectiveness of the proposed results

Study on the Performance of CO2 Two-stage Rotary Compressor in Freezing and Cold Storage Conditions

This paper describes a new type CO2 two-stage rotary compressor for cold storage and freezing of food. A two-stage compression form with an upper cylinder (first-stage) and a lower cylinder (second-stage), unique oil road structures and technical parameters have been used in the rotary compressor to increase the performance. The results indicating that the optimized CO2 two-stage rotary compressor has a significant performance advantage, which the coefficient of performance (COP) increases by 4.4% ~ 6.7%

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

2,5,11,14-Tetra­oxa-8-aza­dispiro­[13.4.0]nona­deca-15,17,19-triene

The title compound, C14H21NO4, has been synthesized from o-dihydroxy­benzene by a three-step reaction. There are two chemically equal but crystallographically independent mol­ecules in the asymmetric unit. The crystal packing is governed by C—H⋯O hydrogen bonds and C—H⋯π inter­actions, forming an infinite network