Data crawling refers to the automated process of acquiring and storing web information,with data crawlers being one of its most widely used forms.The"webpage acquisition"-"webpage filtering"-"...Data crawling refers to the automated process of acquiring and storing web information,with data crawlers being one of its most widely used forms.The"webpage acquisition"-"webpage filtering"-"webpage storage"method of crawling,along with data transactions,often involves breaches of contract,infringements,unfair competition disputes,and other compliance-related legal risks.When courts handle such cases,they have generally adopted the Anti-Unfair Competition Law as the legal basis for regulating data crawling and its subsequent applications,achieving widespread legal consensus.In assessing the scope of unfair competition behavior,the judicial community has widely accepted a moderate extension of the criteria for identifying competitive relationships,and more cases are being adjudicated under the second article of the Anti-Unfair Competition Law.Courts generally use the Anti-Unfair Competition Law as the legal framework when reviewing data crawling behavior,while also emphasizing the balancing of multiple interests.At the same time,challenges arise in case rulings regarding the identification of competitive relationships,damage caused by competition,and the determination of business ethics,which necessitate the optimization of existing criteria and the introduction of new standards to enhance their recognizability and operability.Furthermore,when applying the general provisions of the Anti-Unfair Competition Law,judicial difficulties arise,calling for a return to the competitive law nature,achieving a regulatory model under dynamic competition,and introducing economic analysis standards to enhance the predictability of business ethics judgments.展开更多
Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with...Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with traffic data collected by discrete loop detectors as well as the web-crawl weather data. Matched case-control method and support vector machines （SVMs） technique were employed to identify the risk status. The adaptive synthetic over-sampling technique was applied to solve the imbalanced dataset issues. Random forest technique was applied to select the contributing factors and avoid the over-fitting issues. The results indicate that the SVMs classifier could successfully classify 76.32% of the crashes on the test dataset and 87.52% of the crashes on the overall dataset, which were relatively satisfactory compared with the results of the previous studies. Compared with the SVMs classifier without the data, the SVMs classifier with the web-crawl weather data increased the crash prediction accuracy by 1.32% and decreased the false alarm rate by 1.72%, showing the potential value of the massive web weather data. Mean impact value method was employed to evaluate the variable effects, and the results are identical with the results of most of previous studies. The emerging technique based on the discrete traffic data and web weather data proves to be more applicable on real- time safety management on freeways.展开更多
文摘Data crawling refers to the automated process of acquiring and storing web information,with data crawlers being one of its most widely used forms.The"webpage acquisition"-"webpage filtering"-"webpage storage"method of crawling,along with data transactions,often involves breaches of contract,infringements,unfair competition disputes,and other compliance-related legal risks.When courts handle such cases,they have generally adopted the Anti-Unfair Competition Law as the legal basis for regulating data crawling and its subsequent applications,achieving widespread legal consensus.In assessing the scope of unfair competition behavior,the judicial community has widely accepted a moderate extension of the criteria for identifying competitive relationships,and more cases are being adjudicated under the second article of the Anti-Unfair Competition Law.Courts generally use the Anti-Unfair Competition Law as the legal framework when reviewing data crawling behavior,while also emphasizing the balancing of multiple interests.At the same time,challenges arise in case rulings regarding the identification of competitive relationships,damage caused by competition,and the determination of business ethics,which necessitate the optimization of existing criteria and the introduction of new standards to enhance their recognizability and operability.Furthermore,when applying the general provisions of the Anti-Unfair Competition Law,judicial difficulties arise,calling for a return to the competitive law nature,achieving a regulatory model under dynamic competition,and introducing economic analysis standards to enhance the predictability of business ethics judgments.
基金supported by the National Natural Science Foundation (71301119)the Shanghai Natural Science Foundation (12ZR1434100)
文摘Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with traffic data collected by discrete loop detectors as well as the web-crawl weather data. Matched case-control method and support vector machines （SVMs） technique were employed to identify the risk status. The adaptive synthetic over-sampling technique was applied to solve the imbalanced dataset issues. Random forest technique was applied to select the contributing factors and avoid the over-fitting issues. The results indicate that the SVMs classifier could successfully classify 76.32% of the crashes on the test dataset and 87.52% of the crashes on the overall dataset, which were relatively satisfactory compared with the results of the previous studies. Compared with the SVMs classifier without the data, the SVMs classifier with the web-crawl weather data increased the crash prediction accuracy by 1.32% and decreased the false alarm rate by 1.72%, showing the potential value of the massive web weather data. Mean impact value method was employed to evaluate the variable effects, and the results are identical with the results of most of previous studies. The emerging technique based on the discrete traffic data and web weather data proves to be more applicable on real- time safety management on freeways.
