Most of the current work on skyline queries mainly dealt with querying static query points over static data sets. With the advances in wireless communication, mobile computing, and positioning technologies, it has become possible to obtain and manage (model, index, query, etc.) the trajectories of moving objects in real life, and consequently the need for continuous skyline query processing has become more and more pressing. In this paper, we address the problem of efficiently maintaining continuous skyline queries which contain both static and dynamic attributes. We present a Multi-level Continuous Skyline Query (MCSQ) algorithm, which basically creates a pre-computed skyline data set, facilitates skyline update, and enhances query running time and performance. Our algorithm in brief proceeds as follows: First, we distinguish the data points that are permanently in the skyline and use them to derive a search bound. Second, we establish a pre-computed data set for dynamic skyline that depends on the number of skyline levels (M) which is later used to update the first (initial) skyline points. Finally, every time the skyline needs to be updated we use the pre-computed data sets of skyline to update the previous skyline set and consequently updating first skyline. Finally, we present experimental results to demonstrate the performance and efficiency of our algorithm.
Taxicabs and rideshare cars nowadays are equipped with GPS devices that enable capturing a large volume of traces. These GPS traces represent the moving behavior of the car drivers. Indeed, the real-time discovery of fraud drivers earlier is a demand for saving the passenger’s life and money. For this purpose, this paper proposes a novel time-based system, namely FraudMove, to discover fraud drivers in real-time by identifying outlier active trips. Mainly, the proposed FraudMove system computes the time of the most probable path of a trip. For trajectory outlier detection, a trajectory is considered an outlier trajectory if its time exceeds the time of this computed path by a specified threshold. FraudMove employs a tunable time window parameter to control the number of checks for detecting outlier trips. This parameter allows FraudMove to trade responsiveness with efficiency. Unlike other related works that wait until the end of a trip to indicate that it was an outlier, FraudMove discovers outlier trips instantly during the trip. Extensive experiments conducted on a real dataset confirm the efficiency and effectiveness of FraudMove in detecting outlier trajectories. The experimental results prove that FraudMove saves the response time of the outlier check process by up to 65% compared to the state-of-the-art systems.
Using medicinal extracts is one of the most important alternative methods for producing nanoparticles, due to it is safe, biocompatible and eco-friendly. Hyoscyamus muticus leaf extract was used in this study for the green synthesis of Magnesium Oxide nanoparticles (MgO NPs) by mixing it with a solution of magnesium nitrate. Several techniques were done to characterize the obtained material, including Scanning Electron Microscopy (SEM), Ultraviolet-Visible (UV-Vis) Spectroscopy, X-ray Diffraction (XRD), and Fourier Transform Infrared Spectroscopy (FTIR). Aspergillus ochraceous (A. ochraceous) and Aspergillus niger (A. niger) were incubated at different temperatures. The results indicated that 27°C and 35°C were the optimum temperatures for the growth, respectively. The effect of MgO NPs on the growth and metabolism (α-amylase) of A. niger and A. ochraceous were studied under optimum temperatures. It was observed by adding MgO NPs at concentrations of 0.25%, 0.5% and 1%; fungal growth was inhibited with 11%, 19% and 89% for A. niger and 67%, 76% and 100% for A. ochraceous. The metabolism of Aspergillus species as α-amylase completely prevented at all concentrations of MgO NPs. The purpose of this research is to compare the effects of climate change factor and MgO NPs on the growth of A. niger and A. ochraceous and α-amylase production.
Nowadays, GPS-enabled devices play a vital role in location-based services and daily basis activities such as: locating where your vehicle is at any given time, recovering a stolen vehicle, monitoring your children's location, and traffic or weather alerts. Indeed, precise locations generated from these GPS devices are a must to ensure that the location-based services work accurately. For this purpose, the discovery of abnormal patterns in spatio-temporal data is a significant topic for many kinds of researchers. In this paper, the methods of spatio-temporal outlier detection are categorized into four types: distance and density-based outlier detection, pattern outlier detection, supervised and semi-supervised learning, and statistical and probabilistic techniques. This chapter describes the datasets used in the approaches of spatio-temporal outlier detection and explores its popular applications. The main contribution of this study is to guide researchers to define research gaps in trajectories outlier detection and choose the proper techniques that cope with their research problems.
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