Due to the prevalence of GPS-enabled devices and wireless communications technologies, spatial trajectories that describe the movement history of moving objects are being generated and accumulated at ...an unprecedented pace. Trajectory data in a database are intrinsically heterogeneous, as they represent discrete approximations of original continuous paths derived using different sampling strategies and different sampling rates. Such heterogeneity can have a negative impact on the effectiveness of trajectory similarity measures, which are the basis of many crucial trajectory processing tasks. In this paper, we pioneer a systematic approach to trajectory calibration that is a process to transform a heterogeneous trajectory dataset to one with (almost) unified sampling strategies. Specifically, we propose an anchor-based calibration system that aligns trajectories to a set of anchor points, which are fixed locations independent of trajectory data. After examining four different types of anchor points for the purpose of building a stable reference system, we propose a geometry-based calibration approach that considers the spatial relationship between anchor points and trajectories. Then a more advanced model-based calibration method is presented, which exploits the power of machine learning techniques to train inference models from historical trajectory data to improve calibration effectiveness. Finally, we conduct extensive experiments using real trajectory datasets to demonstrate the effectiveness and efficiency of the proposed calibration system.
Driven by the advances in location positioning techniques and the popularity of location sharing services, semantic enriched trajectory data have become unprecedentedly available. While finding ...relevant Point-of-Interest (POIs) based on users' locations and query keywords has been extensively studied in the past years, it is largely untouched to explore the keyword queries in the context of semantic trajectory database. In this paper, we study the problem of approximate keyword search in massive semantic trajectories. Given a set of query keywords, an approximate keyword query of semantic trajectory (AKQST) returns k trajectories that contain the most relevant keywords to the query and yield the least travel effort in the meantime. The main difference between AKQST and conventional spatial keyword queries is that there is no query location in AKQST, which means the search area cannot be localized. To capture the travel effort in the context of query keywords, a novel utility function, called spatio-textual utility function, is first defined. Then we develop a hybrid index structure called GiKi to organize the trajectories hierarchically, which enables pruning the search space by spatial and textual similarity simultaneously. Finally an efficient search algorithm and fast evaluation of the minimum value of spatio-textual utility function are proposed. The results of our empirical studies based on real check-in datasets demonstrate that our proposed index and algorithms can achieve good scalability.
Point-of-interest (POI) recommendation has attracted many interests recently because of its significant potential for helping users to explore new places and helping location-based service (LBS) ...providers to carry out precision marketing. Compared with the user-item rating matrix in conventional recommender systems, the user-location check-in matrix in POI recommendation is usually much more sparse, which makes the notorious cold start problem more prominent in POI recommendation. Trust-oriented recommendation is an effective way to deal with this problem but it requires that the recommender has access to user check-in and trust data. In practice, however, these data are usually owned by different businesses who are not willing to share their data with the recommender mainly due to privacy and legal concerns. In this paper, we propose a privacy-preserving framework to boost data owners willingness to share their data with untrustworthy businesses. More specifically, we utilize partially homomorphic encryption to design two protocols for privacy-preserving trust-oriented POI recommendation. By offline encryption and parallel computing, these protocols can efficiently protect the private data of every party involved in the recommendation. We prove that the proposed protocols are secure against semi-honest adversaries. Experiments on both synthetic data and real data show that our protocols can achieve privacy-preserving with acceptable computation and communication cost.
Etiological carriers and the excretion of the pathogens causing hand, foot, and mouth disease (HFMD) in healthy persons, patients, and asymptomatic persons infected with HFMD as ongoing infection ...sources may play an important role in perpetuating and spreading epidemics of HFMD. The aims of this study were to determine the carrier status of EV-A71 and CV-A16 in healthy populations, as well as the duration of EV-A71 and CV-A16 shedding in the stools of HFMD patients in an epidemic area of southwest China. A cross-sectional study and a follow-up study were conducted in three HFMD endemic counties of Yunnan Province. Six hundred sixty-seven healthy subjects were recruited to participate in the cross-sectional study, and two stool specimens were collected from each subject. Among the healthy subjects, 90 (13.5%) tested positive for viral isolation, but neither EV-A71 nor CV-A16 was detected in healthy individuals. Of the 150 patients with probable HFMD, 55.3% (83/150) tested positive for viral isolation with presented serotypes such as EV-A71 (51.81%, 43/83), CV-A16 (32.53%, 27/83), other EVs (13.25%, 11/83), and mixed EV-A71 and CV-A16 (2.41%, 2/83). The longest duration of EV-A71 and CV-A16 shedding in stool specimens from patients with HFMD was >46 days after onset. The positive rate of EV-A71 in the stool specimens of confirmed patients dropped to 50% by the end of the third week, and the same occurred with CV-A16 by the end of approximately the seventh week after onset. Although carriers of major causative agents of HFMD in healthy populations are fewer in number, the prolonged shedding of pathogens in patients with HFMD may serve as an important factor in perpetuating and spreading HFMD epidemics.
We identify relation completion (RC) as one recurring problem that is central to the success of novel big data applications such as Entity Reconstruction and Data Enrichment. Given a semantic ...relation ℜ, RC attempts at linking entity pairs between two entity lists under the relation ℜ. To accomplish the RC goals, we propose to formulate search queries for each query entity α based on some auxiliary information, so that to detect its target entity β from the set of retrieved documents. For instance, a pattern-based method (PaRE) uses extracted patterns as the auxiliary information in formulating search queries. However, high-quality patterns may decrease the probability of finding suitable target entities. As an alternative, we propose CoRE method that uses context terms learned surrounding the expression of a relation as the auxiliary information in formulating queries. The experimental results based on several real-world web data collections demonstrate that CoRE reaches a much higher accuracy than PaRE for the purpose of RC.
Schema Matching (SM) and Record Matching (RM) are two necessary steps in integrating multiple relational tables of different schemas, where SM unifies the schemas and RM detects records referring to ...the same real-world entity. The two processes have been thoroughly studied separately, but few attention has been paid to the interaction of SM and RM. In this work, we find that, even alternating them in a simple manner, SM and RM can benefit from each other to reach a better integration performance (i.e., in terms of precision and recall). Therefore, combining SM and RM is a promising solution for improving data integration. To this end, we define novel matching rules for SM and RM, respectively, that is, every SM decision is made based on intermediate RM results, and vice versa, such that SM and RM can be performed alternately. The quality of integration is guaranteed by a Matching Likelihood Estimation model and the control of semantic drift, which prevent the effect of mismatch magnification. To reduce the computational cost, we design an index structure based on q-grams and a greedy search algorithm that can reduce around 90 percent overhead of the interaction. Extensive experiments on three data collections show that the combination and interaction between SM and RM significantly outperforms previous works that conduct SM and RM separately.
Coronary heart disease (CHD) is a dynamic process in which there are interactions between endothelial dysfunction, oxidative stress, and inflammatory responses. The aim of the present study was to ...investigate the function and mechanism of HSCARG in the treatment of CHD.
Male apolipoprotein E/low-density lipoprotein receptor-deficient mice were given a high-fat diet with 21% fat and 0.15% cholesterol for the in vivo model. Human umbilical vein endothelial cells were incubated with angiotensin II for the in vitro model. HSCARG expression was inhibited in patients or mice with CHD.
HSCARG reduced oxidative stress in mice with CHD. HSCARG also reduced reactive oxygen species (ROS)-oxidative stress in the in vitro model. HSCARG induced p47phox expression in the in vitro model by NF-κB activity. The regulation of nuclear factor kappa B (NF-κB) activity or p47phox expression participates in the effects of HSCARG in CHD.
Altogether, our data indicate that HSCARG reduced ROS-oxidative stress in in vivo and in vitro models of CHD via p47phox by NF-κB activity and may be a clinical target for CHD.
With the increasing amount of text data stored in relational databases, there is a demand for RDBMS to support keyword queries over text data. As a search result is often assembled from multiple ...relational tables, traditional IR-style ranking and query evaluation methods cannot be applied directly. In this paper, we study the effectiveness and the efficiency issues of answering top-k keyword query in relational database systems. We propose a new ranking formula by adapting existing IR techniques based on a natural notion of virtual document. We also propose several efficient query processing methods for the new ranking method. We have conducted extensive experiments on large-scale real databases using two popular RDBMSs. The experimental results demonstrate significant improvement to the alternative approaches in terms of retrieval effectiveness and efficiency.
With the rapidly increasing availability of uncertain data in many important applications such as location-based services, sensor monitoring, and biological information management systems, ...uncertainty-aware query processing has received a significant amount of research effort from the database community in recent years. In this paper, we investigate a new type of query in the context of uncertain databases, namely uncertain top-k influential sites query (UTkIS query for short), which can be applied in a wide range of application areas such as marketing analysis and mobile services. Since it is not so straightforward to precisely define the semantics of top-k query with uncertain data, in this paper we introduce a novel and more intuitive formulation of the query on the basis of expected rank semantics. To address the efficiency issue caused by possible worlds exploration, we propose effective pruning rules and a divide-and-conquer paradigm such that the number of candidates as well as the number of possible worlds to be considered can be significantly reduced. Finally, we conduct extensive experiments on real data sets to verify the effectiveness and efficiency of the new methods proposed in this paper.
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