How Much of the Web Is Archived?

Although the Internet Archive's Wayback Machine is the largest and most well-known web archive, there have been a number of public web archives that have emerged in the last several years. With varying resources, audiences and collection development policies, these archives have varying levels of overlap with each other. While individual archives can be measured in terms of number of URIs, number of copies per URI, and intersection with other archives, to date there has been no answer to the question "How much of the Web is archived?" We study the question by approximating the Web using sample URIs from DMOZ, Delicious, Bitly, and search engine indexes; and, counting the number of copies of the sample URIs exist in various public web archives. Each sample set provides its own bias. The results from our sample sets indicate that range from 35%-90% of the Web has at least one archived copy, 17%-49% has between 2-5 copies, 1%-8% has 6-10 copies, and 8%-63% has more than 10 copies in public web archives. The number of URI copies varies as a function of time, but no more than 31.3% of URIs are archived more than once per month.Comment: This is the long version of the short paper by the same title published at JCDL'11. 10 pages, 5 figures, 7 tables. Version 2 includes minor typographical correction

Creating Reusable Educational Components: Lessons from DLESE

Reuse of educational materials is integral to many educator tasks, from designing a course to preparing for a lab or class. This article describes a study on the reuse of educational materials in the context of the Digital Library for Earth System Education (DLESE), a community-owned and governed facility offering high-quality teaching and learning resources for Earth system education. The study noted that educational resource designers often do not develop components with reuse in mind, making it more difficult or impossible for other educators to find and use their material, and that the 'findability' and reusability of community-created digital educational resources is highly dependent on the presentational and structural design of the resources themselves. The authors recommend that all resources clearly state the creator's name and contact information, relevant copyright restrictions, the most significant date for the resource (specifying creation or revision), and the intended grade level. Educational levels: Graduate or professional, Graduate or professional, Graduate or professional

Digitometric Services for Open Archives Environments

We describe “digitometric” services and tools that add value to open-access eprint archives using the Open Archives Initiative (OAI) Protocol for Metadata Harvesting. Celestial is an OAI cache and gateway tool. Citebase Search enhances OAI-harvested metadata with linked references harvested from the full-text to provide a web service for citation navigation and research impact analysis. Digitometrics builds on data harvested using OAI to provide advanced visualisation and hypertext navigation for the research community. Together these services provide a modular, distributed architecture for building a “semantic web” for the research literature

NLP-Based Techniques for Cyber Threat Intelligence

In the digital era, threat actors employ sophisticated techniques for which, often, digital traces in the form of textual data are available. Cyber Threat Intelligence~(CTI) is related to all the solutions inherent to data collection, processing, and analysis useful to understand a threat actor's targets and attack behavior. Currently, CTI is assuming an always more crucial role in identifying and mitigating threats and enabling proactive defense strategies. In this context, NLP, an artificial intelligence branch, has emerged as a powerful tool for enhancing threat intelligence capabilities. This survey paper provides a comprehensive overview of NLP-based techniques applied in the context of threat intelligence. It begins by describing the foundational definitions and principles of CTI as a major tool for safeguarding digital assets. It then undertakes a thorough examination of NLP-based techniques for CTI data crawling from Web sources, CTI data analysis, Relation Extraction from cybersecurity data, CTI sharing and collaboration, and security threats of CTI. Finally, the challenges and limitations of NLP in threat intelligence are exhaustively examined, including data quality issues and ethical considerations. This survey draws a complete framework and serves as a valuable resource for security professionals and researchers seeking to understand the state-of-the-art NLP-based threat intelligence techniques and their potential impact on cybersecurity

Designing A General Deep Web Access Approach Based On A Newly Introduced Factor; Harvestability Factor (HF)

The growing need of accessing more and more information draws attentions to huge amount of data hidden behind web forms defined as deep web. To make this data accessible, harvesters have a crucial role. Targeting different domains and websites enhances the need to have a general-purpose harvester which can be applied to different settings and situations. To develop such a harvester, a number of issues should be considered. Among these issues, business domain features, targeted websites' features, and the harvesting goals are the most influential ones. To consider all these elements in one big picture, a new concept, called harvestability factor (HF), is introduced in this paper. The HF is defined as an attribute of a website (HF_w) or a harvester (HF_h) representing the extent to which the website can be harvested or the harvester can harvest. The comprising elements of these factors are different websites' (for HF_w) or harvesters' (for HF_h) features. These features are presented in this paper by gathering a number of them from literature and introducing new ones through the authors' experiments. In addition to enabling websites' or harvesters' designers of evaluating where they products stand from the harvesting perspective, the HF can act as a framework for designing general purpose deep web harvesters. This framework allows filling in the gap in designing general purpose harvesters by focusing on detailed features of deep websites which have effects on harvesting processes. The represented features in this paper provide a thorough list of requirements for designing deep web harvesters which is not done to best of our knowledge in literature in this extent. To validate the effectiveness of HF in practice, it is shown how the HFs' elements can be applied in categorizing deep websites and how this is useful in designing a harvester. To run the experiments, the developed harvester by the authors, is also discussed in this paper

Cloudarmor: Supporting Reputation-Based Trust Management for Cloud Services

Cloud services have become predominant in the current technological era. For the rich set of features provided by cloud services, consumers want to access the services while protecting their privacy. In this kind of environment, protection of cloud services will become a significant problem. So, research has started for a system, which lets the users access cloud services without losing the privacy of their data. Trust management and identity model makes sense in this case. The identity model maintains the authentication and authorization of the components involved in the system and trust-based model provides us with a dynamic way of identifying issues and attacks with the system and take appropriate actions. Further, a trust management-based system provides us with a new set of challenges such as reputation-based attacks, availability of components, and misleading trust feedbacks. Collusion attacks and Sybil attacks form a significant part of these challenges. This paper aims to solve the above problems in a trust management-based model by introducing a credibility model on top of a new trust management model, which addresses these use-cases, and also provides reliability and availability

BlogForever D2.4: Weblog spider prototype and associated methodology

The purpose of this document is to present the evaluation of different solutions for capturing blogs, established methodology and to describe the developed blog spider prototype

Advanced Data Mining Techniques for Compound Objects

Knowledge Discovery in Databases (KDD) is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in large data collections. The most important step within the process of KDD is data mining which is concerned with the extraction of the valid patterns. KDD is necessary to analyze the steady growing amount of data caused by the enhanced performance of modern computer systems. However, with the growing amount of data the complexity of data objects increases as well. Modern methods of KDD should therefore examine more complex objects than simple feature vectors to solve real-world KDD applications adequately. Multi-instance and multi-represented objects are two important types of object representations for complex objects. Multi-instance objects consist of a set of object representations that all belong to the same feature space. Multi-represented objects are constructed as a tuple of feature representations where each feature representation belongs to a different feature space. The contribution of this thesis is the development of new KDD methods for the classification and clustering of complex objects. Therefore, the thesis introduces solutions for real-world applications that are based on multi-instance and multi-represented object representations. On the basis of these solutions, it is shown that a more general object representation often provides better results for many relevant KDD applications. The first part of the thesis is concerned with two KDD problems for which employing multi-instance objects provides efficient and effective solutions. The first is the data mining in CAD parts, e.g. the use of hierarchic clustering for the automatic construction of product hierarchies. The introduced solution decomposes a single part into a set of feature vectors and compares them by using a metric on multi-instance objects. Furthermore, multi-step query processing using a novel filter step is employed, enabling the user to efficiently process similarity queries. On the basis of this similarity search system, it is possible to perform several distance based data mining algorithms like the hierarchical clustering algorithm OPTICS to derive product hierarchies. The second important application is the classification and search for complete websites in the world wide web (WWW). A website is a set of HTML-documents that is published by the same person, group or organization and usually serves a common purpose. To perform data mining for websites, the thesis presents several methods to classify websites. After introducing naive methods modelling websites as webpages, two more sophisticated approaches to website classification are introduced. The first approach uses a preprocessing that maps single HTML-documents within each website to so-called page classes. The second approach directly compares websites as sets of word vectors and uses nearest neighbor classification. To search the WWW for new, relevant websites, a focused crawler is introduced that efficiently retrieves relevant websites. This crawler minimizes the number of HTML-documents and increases the accuracy of website retrieval. The second part of the thesis is concerned with the data mining in multi-represented objects. An important example application for this kind of complex objects are proteins that can be represented as a tuple of a protein sequence and a text annotation. To analyze multi-represented objects, a clustering method for multi-represented objects is introduced that is based on the density based clustering algorithm DBSCAN. This method uses all representations that are provided to find a global clustering of the given data objects. However, in many applications there already exists a sophisticated class ontology for the given data objects, e.g. proteins. To map new objects into an ontology a new method for the hierarchical classification of multi-represented objects is described. The system employs the hierarchical structure of the ontology to efficiently classify new proteins, using support vector machines