The usage of classification systems is a standard method in libraries to organize all kind of materials. The Dewey Decimal Classification System (DDC) is widely used for this task. Even though approaches exist since the 1970s to automate this classification task, it is most often still a time consuming manual process. With the constantly increasing number of publications the need for automation support is growing. Current approaches have certain limitations e.g. only mono- or bi-lingual support, limited accuracy for research domains, limited to higher levels in the DDC hierarchies. The usage of Large Language Models (LLMs) opens new possibilities to support librarians in their work. In this paper we present preliminarily a study to evaluate the usage of BERT to handle a DDC classification task in the linguistic domain. In addition, we analyze the effect of a more condensed representation of full text on the performance of LLMs for this task. Results on multilingual texts are comparable to recent performances on monolingual inputs.

Web archives are an essential information source for research on historical events. However, the large scale and heterogeneity of web archives make it difficult for researchers to access relevant event-specific materials. In this chapter, we discuss methods for creating event-centric collections from large-scale web archives. These methods are manifold and may require manual curation, adopt search or deploy focused crawling. In this chapter, we focus on the crawl-based methods that identify relevant documents in and across web archives and include link networks as context in the resulting collections.

This book provides practical information about web archives, offers inspiring examples for web archivists, raises new challenges, and shares recent research results about access methods to explore information from the past preserved by web archives. The book is structured in six parts. Part 1 advocates for the importance of web archives to preserve our collective memory in the digital era, demonstrates the problem of web ephemera and shows how web archiving activities have been trying to address this challenge. Part 2 then focuses on different strategies for selecting web content to be preserved and on the media types that different web archives host. It provides an overview of efforts to address the preservation of web content as well as smaller-scale but high-quality collections of social media or audiovisual content. Next, Part 3 presents examples of initiatives to improve access to archived web information and provides an overview of access mechanisms for web archives designed to be used by humans or automatically accessed by machines. Part 4 presents research use cases for web archives. It also discusses how to engage more researchers in exploiting web archives and provides inspiring research studies performed using the exploration of web archives. Subsequently, Part 5 demonstrates that web archives should become crucial infrastructures for modern connected societies. It makes the case for developing web archives as research infrastructures and presents several inspiring examples of added-value services built on web archives. Lastly, Part 6 reflects on the evolution of the web and the sustainability of web archiving activities. It debates the requirements and challenges for web archives if they are to assume the responsibility of being societal infrastructures that enable the preservation of memory. This book targets academics and advanced professionals in a broad range of research areas such as digital humanities, social sciences, history, media studies and information or computer science. It also aims to fill the need for a scholarly overview to support lecturers who would like to introduce web archiving into their courses by offering an initial reference for students.

Web archives constitute an increasingly important source of information for computer scientists, humanities researchers and journalists interested in studying past events. However, currently there are no access methods that help Web archive users to efficiently access event-centric information in large-scale archives that go beyond the retrieval of individual disconnected documents. In this article, we tackle the novel problem of extracting interlinked event-centric document collections from large-scale Web archives to facilitate an efficient and intuitive access to information regarding past events. We address this problem by: (1) facilitating users to define event-centric document collections in an intuitive way through a Collection Specification; (2) development of a specialised extraction method that adapts focused crawling techniques to the Web archive settings; and (3) definition of a function to judge the relevance of the archived documents with respect to the Collection Specification taking into account the topical and temporal relevance of the documents. Our extended experiments on the German Web archive (covering a time period of 19 years) demonstrate that our method enables efficient extraction of event-centric collections for different event types.

Due to technical constrains of the past, metadata in languages written with non-Latin scripts have frequently been entered using various systems of transcription. While this transcription is essential for data curators who may not be familiar with the source script, it is often an encumbrance for researchers in discovery and retrieval. Until 2011 the Judaica collection in Hebrew and Yiddish of the University Library J. C. Senckenberg were catalogued with transcription only. The aim of this work is to develop an open-source system to aid in the automatic conversion of Hebrew transcription back into Hebrew script, using a multi-faceted approach.

BIOfid is a specialized information service currently being developed to mobilize biodiversity data dormant in printed historical and modern literature and to offer a platform for open access journals on the science of biodiversity. Our team of librarians, computer scientists and biologists produce high-quality text digitizations, develop new text-mining tools and generate detailed ontologies enabling semantic text analysis and semantic search by means of user-specific queries. In a pilot project we focus on German publications on the distribution and ecology of vascular plants, birds, moths and butterflies extending back to the Linnaeus period about 250 years ago. The three organism groups have been selected according to current demands of the relevant research community in Germany. The text corpus defined for this purpose comprises over 400 volumes with more than 100,000 pages to be digitized and will be complemented by journals from other digitization projects, copyright-free and project-related literature. With TextImager (Natural Language Processing & Text Visualization) and TextAnnotator (Discourse Semantic Annotation) we have already extended and launched tools that focus on the text-analytical section of our project. Furthermore, taxonomic and anatomical ontologies elaborated by us for the taxa prioritized by the project’s target group - German institutions and scientists active in biodiversity research - are constantly improved and expanded to maximize scientific data output. Our poster describes the general workflow of our project ranging from literature acquisition via software development, to data availability on the BIOfid web portal (http://biofid.de/), and the implementation into existing platforms which serve to promote global accessibility of biodiversity data.

Web archives are typically very broad in scope and extremely large in scale. This makes data analysis appear daunting, especially for non-computer scientists. These collections constitute an increasingly important source for researchers in the social sciences, the historical sciences and journalists interested in studying past events. However, there are currently no access methods that help users to efficiently access information, in particular about specific events, beyond the retrieval of individual disconnected documents. Therefore we propose a novel method to extract event-centric document collections from large scale Web archives. This method relies on a specialized focused extraction algorithm. Our experiments on the German Web archive (covering a time period of 19 years) demonstrate that our method enables the extraction of event-centric collections for different event types.

Web archives constitute a valuable source for researchers from many disciplines. However, their sheer size, the typically broad scope and their temporal dimension make them difficult to work with. We have identified three approaches to access and explore Web archives from different perspectives: user-, data- and graph-centric. In this paper we present related works on these three views as well as discuss their relations and potential synergies. Finally, we propose a generic analysis schema that outlines a systematic way to study Web archives by approaching them from different zoom levels corresponding to the three presented views.

We present a method for detecting word sense changes by utilizing automatically induced word senses. Our method works on the level of individual senses and allows a word to have e.g. one stable sense and then add a novel sense that later experiences change. Senses are grouped based on polysemy to find linguistic concepts and we can find broadening and narrowing as well as novel (polysemous and homonymic) senses. We evaluate on a testset, present recall and estimates of the time between expected and found change.

With advances in technology and culture, our language changes. We invent new words, add or change meanings of existing words and change names of existing things. Unfortunately, our language does not carry a memory; words, expressions and meanings used in the past are forgotten over time. When searching and interpreting content from archives, language changes pose a great challenge. In this paper, we present results of automatic word sense change detection and show the utility for archive users as well as digital humanities' research. Our method is able to capture changes that relate to the usage and culture of a word that cannot easily be found using dictionaries or other resources.

Web archives capture the history of the Web and are therefore an important source to study how societal developments have been reflected on the Web. However, the large size of Web archives and their temporal nature pose many challenges to researchers interested in working with these collections. In this work, we describe the challenges of working with Web archives and propose the research methodology of extracting and studying sub-collections of the archive focused on specific topics and events. We discuss the opportunities and challenges of this approach and suggest a framework for creating sub-collections.

Linked Open Data (LOD) plays an increasingly important role in the area of digital libraries and archives. It enables better semantic-based access methods and also gives the human reader more insights into linked entities. Since semantics is evolving over time, the temporal dimension has an increasing impact on the applicability of LOD to long-term archives. LOD does not necessarily provide a history. Therefore it is not possible to go back in time and access the document related LOD content at the time of the document creation. In this paper we propose to collect LOD information along with the Web content for Web archives to also ensure a good semantic coverage of aWeb crawl.We discuss use cases and requirements and derive the general approach and related data models for the implementation.

Researchers in the Digital Humanities and journalists need to monitor, collect and analyze fresh online content regarding current events such as the Ebola outbreak or the Ukraine crisis on demand. However, existing focused crawling approaches only consider topical aspects while ignoring temporal aspects and therefore cannot achieve thematically coherent and fresh Web collections. Especially Social Media provide a rich source of fresh content, which is not used by state-of-the-art focused crawlers. In this paper we address the issues of enabling the collection of fresh and relevant Web and Social Web content for a topic of interest through seamless integration of Web and Social Media in a novel integrated focused crawler. The crawler collects Web and Social Media content in a single system and exploits the stream of fresh Social Media content for guiding the crawler.

The large size of the Web makes it infeasible for many institutions to collect, store and process archives of the entire Web. Instead, many institutions focus on creating archives of specific subsets of the Web. These subsets may be based around specific topics or events. Our iCrawl system provides a focused crawler that is able to automatically collect Web pages relevant to a topic based on content similarity. Recently, the archiving of Social Media platforms like Twitter has become relevant. Our system can conduct integrated crawls that collect Web pages and Social Media posts concurrently. During such a crawl newly discovered URLs are exchanged between the crawling subsystems. We built the system with the goal to enable domain experts to create archives for their topics of interest. Therefore the system is highly automated and provides support for specifying and conducting crawls. We will demonstrate an easy to use interface for crawl specification that allows users to find seed URLs as well as descriptive keywords using Web and Social Media search APIs. The iCrawl system is available as Open Source software.

Collections of Web documents about specific topics are needed for many areas of current research. Focused crawling enables the creation of such collections on demand. Current focused crawlers require the user to manually specify starting points for the crawl (seed URLs). These are also used to describe the expected topic of the collection. The choice of seed URLs influences the quality of the resulting collection and requires a lot of expertise. In this demonstration we present the iCrawl Wizard, a tool that assists users in defining focused crawls efficiently and semi-automatically. Our tool uses major search engines and Social Media APIs as well as information extraction techniques to find seed URLs and a semantic description of the crawl intent. Using the iCrawl Wizard even non-expert users can create semantic specifications for focused crawlers interactively and efficiently.

Advancements in technology and culture lead to changes in our language. These changes create a gap between the language known by users and the language stored in digital archives. It affects user’s possibility to firstly find content and secondly interpret that content. In a previous work, we introduced our approach for named entity evolution recognition (NEER) in newspaper collections. Lately, increasing efforts in Web preservation have led to increased availability of Web archives covering longer time spans. However, language on the Web is more dynamic than in traditional media and many of the basic assumptions from the newspaper domain do not hold for Web data. In this paper we discuss the limitations of existing methodology for NEER. We approach these by adapting an existing NEER method to work on noisy data like the Web and the Blogosphere in particular. We develop novel filters that reduce the noise and make use of Semantic Web resources to obtain more information about terms. Our evaluation shows the potentials of the proposed approach.

Das World Wide Web ist als weltweites Informations- und Kommunikationsmedium etabliert. Neue Technologien erweitern regelmäßig die Nutzungsformen und erlauben es auch unerfahrenen Nutzern, Inhalte zu publizieren oder an Diskussionen teilzunehmen. Daher wird das Web auch als eine gute Dokumentation der heutigen Gesellschaft angesehen. Aufgrund seiner Dynamik sind die Inhalte des Web vergänglich und neue Technologien und Nutzungsformen stellen regelmässig neue Herausforderungen an die Sammlung von Webinhalten für die Webarchivierung. Dominierten in den Anfangstagen der Webarchivierung noch statische Seiten, so hat man es heute häufig mit dynamisch generierten Inhalten zu tun, die Informationen aus verschiedenen Quellen integrieren. Neben dem klassischen domainorientieren Webharvesting kann auch ein steigendes Interesse aus verschiedenen Forschungsdisziplinen an thematischen Webkollektionen und deren Nutzung und Exploration beobachtet werden. In diesem Artikel werden einige Herausforderungen und Lösungsansätze für die Sammlung von thematischen und dynamischen Inhalten aus dem Web und den sozialen Medien vorgestellt. Des Weiteren werden aktuelle Probleme der wissenschaftlichen Nutzung diskutiert und gezeigt, wie Webarchive und andere temporale Kollektionen besser durchsucht werden können. The World Wide Web is well established as a global information and communication medium. New technologies regularly come along which expand the forms of use and permit even inexperienced users to publish content or take part in discussions. For this reason the Web can also be seen as a good documenter of present- day society. The dynamism of the Web means that its content is, by its very nature, transitory, and new technologies and forms of use regularly present new challenges for the collection of web content for web archiving. Static pages still dominated in the early days of web archiving, whereas many dynamic types of content have now arisen which integrate information from different sources. There is now growing interest from various research disciplines in conventional domain-oriented web harvesting, in thematic web collections and in their use and exploration. This article examines a number of challenges and possible methods of collecting thematic and dynamic content from the Web and social media. Current problems which have arisen in academic use are discussed, and it is shown how web archives and other temporal collections can be searched more effectively.

Preservation and curation of digital materials is a significant contemporary cultural, economic and social issue, yet it is often neglected. For decades, the amount of content created digitally has grown dramatically and more recently exponentially as digital process have become core aspects of everyday business and life. Indeed, the complete life cycle of most information nowadays tends to remains digital. A selection of this content is expected to be of value for the future and can thus be considered being part of our cultural heritage and essential as evidence for accountability over time. As soon as these digital materials, whether they be publications, data sets, or records of transactions, become obsolete, but are still deemed to be of value in the future, they have to be transferred smoothly into appropriate archival information systems (AIS) where they can be kept accessible against a backdrop of changing technologies, information practices, and documentation standards.

Long-term Web archives comprise Web documents gathered over longer time periods and can easily reach hundreds of terabytes in size. Semantic annotations such as named entities can facilitate intelligent access to the Web archive data. However, the annotation of the entire archive content on this scale is often infeasible. The most efficient way to access the documents within Web archives is provided through their URLs, which are typically stored in dedicated index files. The URLs of the archived Web documents can contain semantic information and can offer an efficient way to obtain initial semantic annotations for the archived documents. In this paper, we analyse the applicability of semantic analysis techniques such as named entity extraction to the URLs in a Web archive. We evaluate the precision of the named entity extraction from the URLs in the GermanWeb dataset and analyse the proportion of the archived URLs from 1,444 popular domains in the time interval from 2000 to 2012 to which these techniques are applicable. Our results demonstrate that named entity recognition can be successfully applied to a large number of URLs in our Web archive and provide a good starting point to efficiently annotate large scale collections of Web documents.

The concept of culturomics was born out of the availability of massive amounts of textual data and the interest to make sense of cultural and language phenomena over time. Thus far however, culturomics has only made use of, and shown the great potential of, statistical methods. In this paper, we present a vision for a knowledge-based culturomics that complements traditional culturomics. We discuss the possibilities and challenges of combining knowledge-based methods with statistical methods and address major challenges that arise due to the nature of the data; diversity of sources, changes in language over time as well as temporal dynamics of information in general. We address all layers needed for knowledge-based culturomics, from natural language processing and relations to summaries and opinions.

The web and the social web play an increasingly important role as an information source for Members of Parliament and their assistants, journalists, political analysts and researchers. It provides important and crucial background information, like reactions to political events and comments made by the general public. The case study presented in this paper is driven by two European parliaments (the Greek and the Austrian parliament) and targets an effective exploration of political web archives. In this paper, we describe semantic technologies deployed to ease the exploration of the archived web and social web content and present evaluation results.

Many complex operations involving aircraft, vehicles and persons are carried out at outdoor airport premises. Video surveillance is used for safety and security purposes, but manual monitoring is inefficient and privacy protection must be guaranteed. In this paper, we propose a system named ASEV that automatically assesses situations for airport surveillance. It combines four main components: a low-level image processing unit based on a new hardware implementation to extract features in real time, a high-level image processing unit for scene analysis, a real-time inference engine for scene understanding, and a data protection stage for log encryption. In addition, four often neglected aspects are successfully addressed: two-way communication between system and operator, power consumption, monitored people privacy and operator activity control. Extensive evaluation at a real airport shows that the proposed system improves the operator performance with sound and visual alerts based on the automatic assessment of a large set of different events.

The evolution of named entities affects exploration and retrieval tasks in digital libraries. An information retrieval system that is aware of name changes can actively support users in finding former occurrences of evolved entities. However, current structured knowledge bases, such as DBpedia or Freebase, do not provide enough information about evolutions, even though the data is available on their resources, like Wikipedia. Our Evolution Base prototype will demonstrate how excerpts describing name evolutions can be identified on these websites with a promising precision. The descriptions are classified by means of models that we trained based on a recent analysis of named entity evolutions on Wikipedia.

Working with Web archives raises a number of issues caused by their temporal characteristics. Depending on the age of the content, additional knowledge might be needed to find and understand older texts. Especially facts about entities are subject to change. Most severe in terms of information retrieval are name changes. In order to find entities that have changed their name over time, search engines need to be aware of this evolution. We tackle this problem by analyzing Wikipedia in terms of entity evolutions mentioned in articles regardless the structural elements. We gathered statistics and automatically extracted minimum excerpts covering name changes by incorporating lists dedicated to that subject. In future work, these excerpts are going to be used to discover patterns and detect changes in other sources. In this work we investigate whether or not Wikipedia is a suitable source for extracting the required knowledge.

Accessing Web archives raises a number of issues caused by their temporal characteristics. Additional knowledge is needed to find and understand older texts. Especially entities mentioned in texts are subject to change. Most severe in terms of information retrieval are name changes. In order to find entities that have changed their name over time, search engines need to be aware of this evolution. We tackle this problem by analyzing Wikipedia in terms of entity evolutions mentioned in articles. We present statistical data on excerpts covering name changes, which will be used to discover similar text passages and extract evolution knowledge in future work.

The World Wide Web is the largest information repository available today. However, this information is very volatile and Web archiving is essential to preserve it for the future. Existing approaches to Web archiving are based on simple definitions of the scope of Web pages to crawl and are limited to basic interactions with Web servers. The aim of the ARCOMEM project is to overcome these limitations and to provide flexible, adaptive and intelligent content acquisition, relying on social media to create topical Web archives. In this article, we focus on ARCOMEM’s crawling architecture. We introduce the overall architecture and we describe its modules, such as the online analysis module, which computes a priority for the Web pages to be crawled, and the Application-Aware Helper which takes into account the type of Web sites and applications to extract structure from crawled content. We also describe a large-scale distributed crawler that has been developed, as well as the modifications we have implemented to adapt Heritrix, an open source crawler, to the needs of the project. Our experimental results from real crawls show that ARCOMEM’s crawling architecture is effective in acquiring focused information about a topic and leveraging the information from social media.

The constantly growing amount ofWeb content and the success of the SocialWeb lead to increasing needs for Web archiving. These needs go beyond the pure preservationo of Web pages. Web archives are turning into “community memories” that aim at building a better understanding of the public view on, e.g., celebrities, court decisions and other events. Due to the size of the Web, the traditional “collect-all” strategy is in many cases not the best method to build Web archives. In this paper, we present the ARCOMEM (From Future Internet 2014, 6 689 Collect-All Archives to Community Memories) architecture and implementation that uses semantic information, such as entities, topics and events, complemented with information from the Social Web to guide a novel Web crawler. The resulting archives are automatically enriched with semantic meta-information to ease the access and allow retrieval based on conditions that involve high-level concepts..

Today an increasing interest in collecting, analyzing and preserving Web content can be observed in many digital humanities. Especially the Social Web is attractive for many humanities disciplines as it provides a direct access to statements of many people about politics, popular topics or events. In this paper we present an exemplary study that we have conducted with the aim to understand the needs of scientists in social sciences, historical sciences and law with respect to the creation of Web archives.

The microblogging service Twitter has become one of the most popular sources of real time information. Every second, hundreds of URLs are posted on Twitter. Due to the maximum tweet length of 140 characters, these URLs are in most cases a shortened version of the original URLs. In contrast to the original URLS, which usually provide some hints on the destination Web site and the specific page, shortened links do not tell the users what to expect behind them. These links might contain relevant information or news regarding a certain topic of interest, but they might just as well be completely irrelevant, or even lead to a malicious or harmful website. In this paper, we present our work towards identifying credible Twitter users for given topics. We achieve this by characterizing the content of the posted URLs to further relate to the expertise of Twitter users.

The introduction of Social Media allowed more people to publish texts by removing barriers that are technical but also social such as the editorial controls that exist in traditional media. The resulting language tends to be more like spoken language because people adapt their use to the medium. Since spoken language is more dynamic, more new and short lived terms are introduced also in written format on the Web. In teTahmasebi2012 we presented an unsupervised method for Named Entity Evolution Recognition (NEER) to find name changes in newspaper collections. In this paper we present BlogNEER, an extension to apply NEER on blog data. The language used in blogs is often closer to spoken language than to language used in traditional media. BlogNEER introduces a novel semantic filtering method that makes use of Semantic Web resources (i.e., DBpedia) to gain more information about terms. We present the approach of BlogNEER and initial results that show the potentials of the approach.

As language evolves over time, documents stored in long- term archives become inaccessible to users. Automatically, detecting and handling language evolution will become a necessity to meet user’s information needs. In this paper, we investigate the performance of modern tools and algorithms applied on modern English to find word senses that will later serve as a basis for finding evolution. We apply the curvature clustering algorithm on all nouns and noun phrases extracted from The Times Archive (1785–1985). We use natural language processors for part-of-speech tagging and lemmatization and report on the performance of these processors over the entire period. We evaluate our clusters using WordNet to verify whether they correspond to valid word senses. Because The Times Archive contains OCR errors, we investigate the effects of such errors on word sense discrimination results. Finally, we present a novel approach to correct OCR errors present in the archive and show that the coverage of the curvature clustering algorithm improves. We increase the number of clusters by 24 %. To verify our results, we use the New York Times corpus (1987–2007), a recent collection that is considered error free, as a ground truth for our experiments. We find that after correcting OCR errors in The Times Archive, the performance of word sense discrimination applied on The Times Archive is comparable to the ground truth.

With advancements in technology and culture, our language changes. We invent new words, add or change meanings of existing words and change names of existing things. Left untackled, these changes in language create a gap between the language known by users and the language stored in our digital archives. In particular, they affect our possibility to firstly find content and secondly interpret that content. In this paper we discuss the limitations brought on by language evolution and existing methodology for automatically finding evolution. We discuss measured needed in the near future to ensure semantically accessible digital archives for long-term preservation.

Dynamic selection of Web services at runtime is important for building flexible and loosely-coupled service-oriented applications. An abstract description of the required services is provided at design-time, and matching service offers are located at runtime. With the growing number of Web services that provide the same functionality but differ in quality parameters (e.g., availability, response time), a decision needs to be made on which services should be selected such that the user's end-to-end QoS requirements are satisfied. Although very efficient, local selection strategy fails short in handling global QoS requirements. Solutions based on global optimization, on the other hand, can handle global constraints, but their poor performance renders them inappropriate for applications with dynamic and realtime requirements. In this article we address this problem and propose a hybrid solution that combines global optimization with local selection techniques to benefit from the advantages of both worlds. The proposed solution consists of two steps: first, we use mixed integer programming (MIP) to find the optimal decomposition of global QoS constraints into local constraints. Second, we use distributed local selection to find the best Web services that satisfy these local constraints. The results of experimental evaluation indicate that our approach significantly outperforms existing solutions in terms of computation time while achieving close-to-optimal results.

With the rapidly increasing pace at which Web content is evolving, particularly social media, preserving the Web and its evolution over time becomes an important challenge. Meaningful analysis of Web content lends itself to an entity-centric view to organise Web resources according to the information objects related to them. Therefore, the crucial challenge is to extract, detect and correlate entities from a vast number of heterogeneous Web resources where the nature and quality of the content may vary heavily. While a wealth of information extraction tools aid this process, we believe that, the consolidation of automatically extracted data has to be treated as an equally important step in order to ensure high quality and non-ambiguity of generated data. In this paper we present an approach which is based on an iterative cycle exploiting Web data for (1) targeted archiving/crawling of Web objects, (2) entity extraction, and detection, and (3) entity correlation. The long-term goal is to preserve Web con-tent over time and allow its navigation and analysis based on well-formed struc-tured RDF data about entities.

Semantic ambient media are the novel trend in the world of media reaching from the pioneering subareas such as ambient advertising to the new and emerging subareas such as ambient assisted living. They will likely shape the upcoming years in terms of modeling smart environments and also media consumption and interaction. This work analyzes semantic ambient media by considering business models, content and media, interaction design and consumer experience, and methods and techniques that are important to create this new form of media. Discussion is led using the state-of-the-art event of the semantic ambient media, which is the annual international workshop on Semantic Ambient Media Experience (SAME). The study also creates a vision for how ambient media will evolve and how they will look like in the future decade.

The term ambient media was in its beginning used only for ambient advertising. Nowadays it denotes the media environment and the communication of information in ubiquitous and pervasive environments. With the addition of intelligence, the new field of semantic ambient media was established. In recent years, the field of semantic ambient media has spread its span from only a few sub-areas, such as ambient advertising, to new ones, such as ambient-assisted living (AAL) and health-monitoring media , significantly supported by intelligence. The study presented in this paper provides an advanced introduction to the field of semantic ambient media including the solutions for threat issues and illustration of success stories of the field. It conducts a survey of the related work and presents a thorough discussion of it. The related work is grouped according to the coverage of the principles of semantic ambient media. Based on the state-of-the-art research, the future possibilities of the field are demonstrated, especially for the ambient-assisted living, audio-visual rendering of media objects, user design principles and the society impact of the field. The paper provides ideas for impacting ambient media and directions and questions for further research. It also discusses the potential of the combination of several research studies.

The ARCOMEM project is about memory institutions like archives, museums and libraries in the age of the Social Web. Social media are becoming more and more pervasive in all areas of life. ARCOMEM's aim is to help to transform archives into collective memories that are more tightly integrated with their community of users and to exploit Web 2.0 and the wisdom of crowds to make Web archiving a more selective and meaning-based process. ARCOMEM (FP7-IST-270239) is an Integrating Project in the FP7 program of the European Commission, which involves twelve partners from academia, industry and public sector. The project will run from January 1, 2011 to December 31, 2013.

The constantly growing amount of Web content and the success of the Social Web lead to increasing needs for Web archiving. These needs go beyond the pure preservation of Web pages. Web archives are turning into “community memories” that aim at building a better understanding of the public view on, e.g., celebrities, court decisions, and other events. In this paper we present the ARCOMEM architecture that uses semantic information such as entities, topics, and events complemented with information from the social Web to guide a novel Web crawler. The resulting archives are automatically enriched with semantic meta-information to ease the access and allow retrieval based on conditions that involve high-level concepts.

High impact events, political changes and new technologies are reflected in our language and lead to constant evolution of terms, expressions and names. Not knowing about names used in the past for referring to a named entity can severely decrease the performance of many computational linguistic algorithms. We propose NEER, an unsupervised method for named entity evolution recognition independent of external knowledge sources. We find time periods with high likelihood of evolution. By analyzing only these time periods using a sliding window co-occurrence method we capture evolving terms in the same context. We thus avoid comparing terms from widely different periods in time and overcome a severe limitation of existing methods for named entity evolution, as shown by the high recall of 90% on the New York Times corpus. We compare several relatedness measures for filtering to improve precision. Furthermore, using machine learning with minimal supervision improves precision to 94%.

Knowing the behavior of terms in written texts can help us tailor fit models, algorithms and resources to improve access to digital libraries and help us answer information needs in longer spanning archives. In this paper we investigate the behavior of English written text in blogs in comparison to traditional texts from the New York Times, The Times Archive, and the British National Corpus. We show that user generated content, similar to spoken content, differs in characteristics from ‘professionally’ written text and experiences a more dynamic behavior.

Knowing about the evolution of a term can significantly help when searching for relevant information, especially in case of sudden evolutions (e.g. as of dramatical changes in political situations). Here, some terms get a completely new meaning or are used in new or different ways. In mobile situations it is important to be able to effectively retrieve information, since this is usually done in a hurry and interaction possibilities with mobile devices are limited. In this paper we describe a methodology using word sense discrimination to discover term evolution. We present two mobile interfaces for easy access and exploration of this evolution, as well as a user-study to show its usefulness. We conclude the paper with an outlook of further research possibilities in this new topic.

We present, compare and contrast new directions in long term digital preservation as covered by the four large European Community funded research projects that started in 2011. The new projects widen the domain of digital preservation from the traditional purview of memory institutions preserving documents to include scenarios such as health-care, data with direct commercial value, and web-based data. Some of these projects consider not only how to preserve the programs needed to interpret the data but also how to manage and preserve the related workflows. Considerations such as risk analysis and cost estimation are built into some of them, and more than one of these efforts is examining the use of cloud-based technologies. All projects look into programmatic solutions, while emphasizing different aspects such as data collection, scalability, reconfigurability, and full lifecycle management. These new directions will make digital preservation applicable to a wider domain of users and will give better tools to assist in the process.

Knowing about language evolution can significantly help to reveal lost information and help access documents containing language that has long since been forgotten. In this position paper we will report on our methods for finding word senses and show how these can be used to reveal important information about their evolution over time. We discuss the weaknesses of current approaches and outline future work to overcome these weaknesses.

More and more national libraries and institutes are archiving the web as a part of the cultural heritage. As with all long term archives, these archives contain text and language that evolves over time. This is particularly true for web archives as content published online is highly dynamic and changing at a fast rate. The language evolution causes gaps between the terminology used for querying and the one stored in long term archives. To ensure access and interpretability of these archives, language evolution must be found and handled in an automatic manner. In this paper we present the LiWA Terminology evolution module, TeVo which takes us one step closer to fully automatic detection of terminology evolution. TeVo consists of a pipeline for finding evolution from web archives based on the UIMA framework. The LiWA TeVo module consists of two main processing chains, the first for Warc file extraction and text processing and the second for finding terminology evolution. We also present the terminology evolution browser, the TeVo browser, which aids in exploring evolution of terms present in archives.

Knowing about the evolution of a term can significantly decrease time needed for searching for information. It can also aid in quickly getting a broader overview, which is essential when one is on the move. In this paper we present a solution for providing language evolution knowledge ``on the go". We present a mobile interface for easy access and visualization as well as an overview of how this evolution was found.

QoS-based service selection aims at finding the best component services that satisfy the end-to-end quality requirements. The problem can be modeled as a multi-dimension multi-choice 0-1 knapsack problem, which is known as NP-hard. Recently published solutions propose using linear programming techniques to solve the problem. However, the poor scalability of linear program solving methods restricts their applicability to small-size problems and renders them inappropriate for dynamic applications with run-time requirements. In this paper, we address this problem and propose a scalable QoS computation approach based on a heuristic algorithm, which decomposes the optimization problem into small sub-problems that can be solved more efficiently than the original problem. Experimental evaluations show that near-to-optimal solutions can be found using our algorithm much faster than using linear programming methods.

An essential issue in peer-to-peer data management is to keep data highly available all the time. The paper presents a replication protocol that adjusts autonomously the number of replicas to deliver a configured data availability guarantee. The protocol is based on a Distributed Hash Table (DHT), measurement of peer online probability in the system, and adjustment of the number of replicas accordingly. The evaluation shows that we are able to maintain the requested data availability achieving near to optimal storage costs, independent of the number of replicas used during the initialization of the system.

The medium is the message! And the message was literacy, media democracy and music charts. Mostly one single distinguishable medium such as TV, the Web, the radio, or books transmitted the message. Now in the age of ubiquitous and pervasive computing, where information flows through a plethora of distributed interlinked media---what is the message ambient media will tell us? What does semantic mean in this context? Which experiences will it open to us? What is content in the age of ambient media? Ambient media are embedded throughout the natural environment of the consumer---in his home, in his car, in restaurants, and on his mobile device. Predominant sample services are smart wallpapers in homes, location based services, RFID based entertainment services for children, or intelligent homes. The goal of this article is to define semantic ambient media and discuss the contributions to the Semantic Ambient Media Experience (SAME) workshop, which was held in conjunction with the ACM Multimedia conference in Vancouver in 2008.

An essential issue in peer-to-peer data management is to keep data highly available all the time. A common idea is to replicate data hoping that at least one replica is available when needed. However, due to churns, the number of created replicas could be not sufficient for guaranteeing the intended data availability. If the number of replicas is computed according to the lowest expected peer availability (a classical case), but the expectation were too high, then the peer availability after a churn could be too low, and the system could not be able to recover the requested data availability. The paper is a continuation of previous work (Knezevic et al., 2006) and presents a replication protocol that delivers a configured data availability guarantee, and is resistant to, or recovers fast from churns. The protocol is based on a distributed hash table (DHT), measurement of peer online probability in the system, and adjustment of the number of replicas accordingly. The evaluation shows that we are able to maintain or recover the requested data availability during or shortly after stronger or weaker churns, and at the same time the storage overhead is close to the theoretical minimum

A key criterion in the design, procurement and use of computer systems is performance. Performance typically means the throughput and response time of a system. The effects of poorly performing systems range from dissatisfied users to high penalties for companies due to missed processing deadlines. As a result of continuously increasing hardware performance, companies often solve performance problems by replacing existing hardware with faster machines. One consequence can be that they achieve a performance increase, but the overall performance increase is less than expected. The reason for this is that the combination of hardware and software does not match. For system designers it would be helpful to have a systematic method which supports them in the design of new systems and in the extension of existing systems. The need for a systematic configuration method is motivated by a typical B2B application from the financial industry. Banks have to deal with several payment messages standards like EDIFACT or S.W.I.F.T. which have to be converted into the banks' internal representation for further processing. Such converters have to handle message size ranging from some 100 bytes to about 60 MB and have to fulfil certain performance requirements. To achieve the performance goals, identification of the hardware and software configuration is an important step in the implementation of a distributed message converter system. This thesis presents a systematic approach for the cost performance analysis of distributed job processing systems based on given requirements on throughput and system response time. Our method allows us to search for suitable configurations while minimizing the use of expensive methods for performance evaluation to the largest degree. The method is organized into a hardware and a software configuration step. For each of these configuration steps algorithms were developed. For the hardware configuration step we first approximate single host performance by a coarse model that requires few, inexpensive to obtain, key parameters. Based on it we perform the hardware selection and determine the workload distribution for the selected host configuration. The workload distribution and the hardware configuration are used to build a Layered Queueing Network model (LQN) of the complete system. It is used to determine a software configuration that actually achieves the performance that has been predicted given the hardware configuration. Since evaluations of the complete model are rather expensive, we use a greedy heuristic, which tries to minimize the number of model evaluations required. We have used our method to configure a large distributed system in order to demonstrate the scalability of the method. For a smaller system configuration we compared the predicted results with real system measurements. The verification on the real system shows that the method could be applied successfully to configure a distributed system to reach maximum performance. As we are using queueing networks for system performance modeling, our system configuration method is based on average system performance values. Hence runtime deviations are not covered during the system design phase and have to be handled during runtime by a scheduler to distribute incoming jobs in an optimal way among the hosts. In our case of the EDI message converter it turned out that the standard online scheduling method doesn't fulfil all requirements. Hence we adapted the Bin Stretching scheduling approach to fulfil the functional requirements of deadline processing and priority processing as well as the system performance requirements of low system response times and high system throughput. The algorithm behavior has been analyzed by simulation in different scenarios corresponding to different message distributions. The simulation results shows that the modified Bin-Stretching strategy generally gives better results than the well known list scheduling in the FCFS variety. We were also able to verify on our real message converter system the general good behavior of our algorithm.

Information and communication infrastructures underwent a rapid and extreme decentralization process over the past decade: From a world of statically and partially connected central servers rose an intricate web of millions of information sources loosely connecting one to another. Today, we expect to witness the extension of this revolution with the wide adoption of meta-data standards like RDF or OWL underpinning the creation of a semantic web. Again, we hope for global properties to emerge from a multiplicity of pair-wise, local interactions, resulting eventually in a self-stabilizing semantic infrastructure. This paper represents an effort to summarize the conditions under which this revolution would take place as well as an attempt to underline its main properties, limitations and possible applications.

With new standards like RDF or OWL paving the way for the much anticipated Semantic Web, a new breed of very large scale semantic systems is about to appear. Traditional semantic reconciliation techniques, dependent upon shared vocabularies or global ontologies, cannot be used in such open and dynamic environments. Instead, new heuristics based on emerging properties and local consensuses have to be exploited in order to foster semantic interoperability in the large. In this paper, we outline the main differences between traditional semantic reconciliation methods and these new heuristics. Also, we characterize the resulting emergent semantics systems and provide a couple of hints vis-à-vis their potential applications.

Finding a configuration of a distributed system satisfying performance goals is a complex search problem that involves many design parameters, like hardware selection, job distribution and process configuration. Performance models are a powerful tool to analyse potential system configurations, however, their evaluation is expensive, such that only a limited number of possible configurations can be evaluated. In this paper we present a systematic method to find a satisfactory configuration with feasible effort, based on a two-step approach. First, performing a queuing network analysis a hardware configuration is determined and then a software configuration is incrementally optimized by simulating Layered Queuing Network models. We applied this method to the design of performant EDI converter systems in the financial domain, where increasing message volumes need to be handled due to the growing importance of B2B interaction.