Recent years have witnessed an unprecedented proliferation of social media. People around the globe author, every day, millions of blog posts, micro-blog posts, social network status updates, etc. This rich stream of information can be used to identify, on an ongoing basis, emerging stories, and events that capture popular attention. Stories can be identified via groups of tightly-coupled realworld entities, namely the people, locations, products, etc., that are involved in the story. The sheer scale, and rapid evolution of the data involved necessitate highly efficient techniques for identifying important stories at every point of time.The main challenge in real-time story identification is the maintenance of dense subgraphs (corresponding to groups of tightlycoupled entities) under streaming edge weight updates (resulting from a stream of user-generated content). This is the first work to study the efficient maintenance of dense subgraphs under such streaming edge weight updates. For a wide range of definitions of density, we derive theoretical results regarding the magnitude of change that a single edge weight update can cause. Based on these, we propose a novel algorithm, DYNDENS, which outperforms adaptations of existing techniques to this setting, and yields meaningful results. Our approach is validated by a thorough experimental evaluation on large-scale real and synthetic datasets.

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User generated content and social media (in the form of blogs, wikis, online video, microblogs, etc) are proliferating online. Grapevine conducts large scale data analysis on the social media collective, distilling and extracting information in real time. It aims to track entities and stories of interest in millions of blog posts, thousands of tweets, news items, etc., daily. Grapevine facilitates the interactive exploration of content, allowing users to discover interesting or surprising stories, optionally narrowed down on a specific demographic of interest (e.g. "What are Torontonians talking about on blogs?", "What are popular stories across news sources in Canada?", "What are financiers in Texas blogging about today?"). Stories of interest can be explored in a variety of ways, such as modifying their scope, obtaining related content (blog posts, news, etc), and examining their temporal evolution.

Text corpora are often enhanced by additional metadata which relate real-world entities, with each document in which such entities are discussed. Such relationships are typically obtained through widely available Information Extraction tools. At the same time, interesting known associations typically hold among these entities. For instance, a corpus might contain discussions on hotels, cities and airlines; fixed associations among these entities may include: airline A operates a flight to city C, hotel H is located in city C.A plethora of applications necessitate the identification of associated entities, each best matching a given set of keywords. Consider the sample query: Find a holiday package in a "pet-friendly" hotel, located in a "historical" yet "lively" city, with travel operated by an "economical" and "safe" airline. These keywords are unlikely to occur in the textual description of entities themselves, (e.g., the actual hotel name or the city name or the airline name). Consequently to answer such queries, one needs to exploit both relationships between entities and documents (e.g., keyword "pet-friendly" occurs in a document that contains an entity specifying a hotel name H), and the known associations between entities (e.g., hotel H is located in city C).In this work, we focus on the class of "entity package finder" queries outlined above. We demonstrate that existing techniques cannot be efficiently adapted to solve this problem, as the resulting algorithm relies on estimations with excessive runtime and/or storage overheads. We propose an efficient algorithm to process such queries, over large corpora. We devise early pruning and termination strategies, in the presence of joins and aggregations (executed on entities extracted from text), that do not depend on any estimates. Our analysis and experimental evaluation on real and synthetic data demonstrates the efficiency and scalability of our approach.

Recent years have witnessed an unprecedented proliferation of social media. People around the globe author, every day, millions of blog posts, micro-blog posts, social network status updates, etc. This rich stream of information can be used to identify, on an ongoing basis, emerging stories, and events that capture popular attention. Stories can be identified via groups of tightly-coupled realworld entities, namely the people, locations, products, etc., that are involved in the story. The sheer scale, and rapid evolution of the data involved necessitate highly efficient techniques for identifying important stories at every point of time.The main challenge in real-time story identification is the maintenance of dense subgraphs (corresponding to groups of tightlycoupled entities) under streaming edge weight updates (resulting from a stream of user-generated content). This is the first work to study the efficient maintenance of dense subgraphs under such streaming edge weight updates. For a wide range of definitions of density, we derive theoretical results regarding the magnitude of change that a single edge weight update can cause. Based on these, we propose a novel algorithm, DYNDENS, which outperforms adaptations of existing techniques to this setting, and yields meaningful results. Our approach is validated by a thorough experimental evaluation on large-scale real and synthetic datasets.

The relentless pace at which textual data are generated on-line necessitates novel paradigms for their understanding and exploration. To this end, we introduce a methodology for discovering strong entity associations in all the slices (metadata value restrictions) of a document collection. Since related documents mention approximately the same group of core entities (people, locations, etc.), the groups of coupled entities discovered can be used to expose themes in the document collection.We devise and evaluate algorithms capable of addressing two flavors of our core problem: algorithm THR-ENT for computing all sufficiently strong entity associations and algorithm TOP-ENT for computing the top-k strongest entity associations, for each slice of the document collection.

Information and specifically Web pages may be organized, indexed, searched, and navigated using various metadata aspects, such as keywords, categories (themes), and also space. While categories and keywords are up for interpretation, space represents an unambiguous aspect to structure information. The basic problem of providing spatial references to content is solved by geocoding; a task that relates identifiers in texts to geographic co-ordinates. This work presents a methodology for the semiautomatic geocoding of persistent Web pages in the form of collaborative human intervention to improve on automatic geocoding results. While focusing on the Greek language and related Web pages, the developed techniques are universally applicable. The specific contributions of this work are (i) automatic geocoding algorithms for phone numbers, addresses and place name identifiers and (ii) a Web browser extension providing a map-based interface for manual geocoding and updating the automatically generated results. With the geocoding of a Web page being stored as respective annotations in a central repository, this overall mechanism is especially suited for persistent Web pages such as Wikipedia. To illustrate the applicability and usefulness of the overall approach, specific geocoding examples of Greek Web pages are presented.

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