Multiagent Algorithms for Optimizing Bundles of Trajectories of Deterministic Systems with Incomplete Instant Feedback

Journal of Computer and Systems Sciences International - The problem of finding the optimal control of bundles of trajectories of continuous deterministic systems with incomplete feedback is...     

On Finding Templates on Web Collections

Templates are pieces of HTML code common to a set of web pages usually adopted by content providers to enhance the uniformity of layout and navigation of theirs Web sites. They are usually generated using authoring/publishing tools or by programs that build HTML pages to publish content from a database. In spite of their usefulness, the content of templates can negatively affect the quality of results produced by systems that automatically process information available in web sites, such as search engines, clustering and automatic categorization programs. Further, the information available in templates is redundant and thus processing and storing such information just once for a set of pages may save computational resources. In this paper, we present and evaluate methods for detecting templates considering a scenario where multiple templates can be found in a collection of Web pages. Most of previous work have studied template detection algorithms in a scenario where the collection has just a single template. The scenario with multiple templates is more realistic and, as it is discussed here, it raises important questions that may require extensions and adjustments in previously proposed template detection algorithms. We show how to apply and evaluate two template detection algorithms in this scenario, creating solutions for detecting multiple templates. The methods studied partitions the input collection into clusters that contain common HTML paths and share a high number of HTML nodes and then apply a single-template detection procedure over each cluster. We also propose a new algorithm for single template detection based on a restricted form of bottom-up tree-mapping that requires only small set of pages to correctly identify a template and which has a worst-case linear complexity. Our experimental results over a representative set of Web pages show that our approach is efficient and scalable while obtaining accurate results.     

Finding seeds to bootstrap focused crawlers

Focused crawlers are effective tools for applications requiring a high number of pages belonging to a specific topic. Several strategies for implementing these crawlers have been proposed in the literature, which aim to improve crawling efficiency by increasing the number of relevant pages retrieved while avoiding non-relevant pages. However, an important aspect of these crawlers has been largely overlooked: the selection of the seed pages that serve as the starting points for a crawl. In this paper, we show that the seeds can greatly influence the performance of crawlers, and propose a new framework for automatically finding seeds. We describe a system that implements this framework and show, through a detailed experimental evaluation, that by providing crawlers a seed set that is large and varied, they not only obtain higher harvest rates but also an improved topic coverage.