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Movement is a complex process that evolves through both space and time. Movement data generated by moving objects is a kind of big data, which has been a focus of research in science, technology, economics, and social studies. Movement database is also at the forefront of geographic information science research. Developing efficient access methods for movement data stored in movement databases is of critical importance. Tree-like indexing structures such as the R-tree, Quadtree, Octree are not suitable for indexing multi-dimensional movement data because they all have high space cost of their inner nodes. In addition, it is difficult to use them for parallel access to multidimensional movement data because they thereof, are in hierarchical structures, which have severe overlapping problems in high dimensional space. In this paper, we propose a novel access method, the Decomposition Tree (D-tree), for indexing multi-dimensional movement data. The D-tree is a virtual tree without inner nodes, instead, through an encoding method based on integer bit-shifting operation, and can efficiently answer a wide range of queries. Experimental results show that the space cost and query performance of D-tree are superior to its best known competitors.

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This study demonstrates the development of a gas chromatography-triple quadrupole tandem mass spectrometry (GC-MS-MS) assay to detect clenbuterol in human urine and the comparison of this method with GC-MS techniques and gas chromatography-high resolution mass spectrometry (GC-HRMS) techniques. Urine samples were hydrolyzed with β-glucuronidase, extracted with methyl tert-butyl ether and dried under nitrogen. The derivative reagent was N-methyl-N-(trimethylsilyl)-trifluoroacetamide with NH4I and was analyzed by GC-MS, GC-MS-MS and GC-HRMS. A validation study was conducted by GC-MS-MS. The analyses of clenbuterol using different mass spectrometric techniques were compared. The limit of detection (LOD) for clenbuterol in human urine was 2 ng/mL by GC-MS (selected ion monitoring mode: SIM mode), 0.06 ng/mL by GC-HRMS and 0.03 ng/mL by GC-MS-MS, respectively, while the LOD by GC-HRMS was 0.06. With GC-MS-MS, the intra-assay and inter-assay precisions were less than 15%, the recoveries were 86 to 112% and the linear range was 0.06 to 8.0 ng/mL. The GC-MS under SIM mode can be used as a screening tool to detect clenbuterol at trace levels in human urine. The GC-MS-MS and GC-HRMS methods can confirm clenbuterol when its concentration is below 2 ng/mL. The results demonstrate that the GC-MS-MS method is quite sensitive, specific and reliable for the detection of clenbuterol in doping analysis.

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Timelines have been used for centuries and have become more and more widely used with the development of social media in recent years. Every day, various smart phones and other instruments on the internet of things generate massive data related to time. Most of these data can be managed in the way of timelines. However, it is still a challenge to effectively and efficiently store, query, and process big timeline data, especially the instant recommendation based on timeline similarities. Most existing studies have focused on indexing spatial and interval datasets rather than the timeline dataset. In addition, many of them are designed for a centralized system. A timeline index structure adapting to parallel and distributed computation framework is in urgent need. In this research, we have defined the timeline similarity query and developed a novel timeline index in the distributed system, called the Distributed Triangle Increment Tree (DTI-Tree), to support the similarity query. The DTI-Tree consists of one T-Tree and one or more TI-Trees based on a triangle increment partition strategy with the Apache Spark. Furthermore, we have provided an open source timeline benchmark data generator, named TimelineGenerator, to generate various timeline test datasets for different conditions. The experiments for DTI-Tree's construction, insertion, deletion, and similarity queries have been executed on a cluster with two benchmark datasets that are generated by TimelineGenerator. The experimental results show that the DTI-tree provides an effective and efficient distributed index solution to big timeline data.