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Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

Semirings are fundamental algebraic structures that in recent times have found a number of applications to computer science, especially in the areas of databases and automata. On the side of databases, commercial query languages, such as SQL, use bag semantics, instead of set semantics, to evaluate relational database queries, which means that the semiring of the natural numbers is used to annotate tuples in the input and output relations. More generally, the annotations can be values in some fixed semiring; this gives a common generalization of both set semantics and bag semantics of database queries, and also makes it possible to model other situations in which one is interested, e.g., in the probability or the reliability of an answer. Furthermore, semirings of polynomials have been successfully used to carry out a rigorous study of provenance in databases. On the side of automata, semirings are used to define weighted automata, which are nondeterministic finite automata augmented with values from a semiring as weights on the transitions. These weights may model, e.g., the cost involved when executing a transition, the amount of resources or time needed for this, or the probability or reliability of its successful execution. Weighted automata have found numerous applications to natural language processing, speech recognition, and algorithms for digital image compression. These applications have inspired numerous investigations in the logic-in-computer-science community.

Informationstheorie und Grundlagen der Informatik,    Softwareentwicklung

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

Process mining and visual analytics are separate disciplines with the common goal of helping humans gain insight into and extract knowledge about relevant phenomena from complex data. Process mining (PM) is a rapidly growing discipline blending machine learning and data mining concepts with ideas taken from the field of business process management (BPM). It utilizes event data recorded by IT systems that support business process execution for a variety of tasks, from the automated discovery of graphical process models to operational support. Visual Analytics (VA) is a multidisciplinary approach that combines interactive, visual, and analytical methods to make complex phenomena more comprehensible, facilitate new insights, and enable knowledge discovery. VA research happens at the intersection of data mining and knowledge discovery, information visualization, human-computer interaction, and cognitive science.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

Data structures are the science of organizing and accessing data, and their study is a core part of computer science. They underpin our computing infrastructure with efficiency being of critical importance. As the computing landscape changes with more demanding tasks arising, data structure research remains vibrant, with two aspects coming particularly in focus: scalability and adaptivity. Scalability means that data structures remain efficient as data sets increase, become more dynamic, and become more distributed. Adaptivity implies taking advantage of modern hardware, such as multicore computation or memory hierarchies, as well specific structure and biases in the operations performed. One seeks to create structures that maximally take advantage of such architectural and distributional details without any foreknowledge of them. General limits of adaptivity have long posed deep theoretical questions, which continue to inspire research. This Dagstuhl Seminar is part of a successful series begun in 1991. The series has contributed to shaping trends in data structures research.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

This summer school is an intensive course on software techniques for data management and database management. Topics include database command translation and query optimization, query execution and indexing, row- and column-oriented storage structures and algorithms, optimistic and pessimistic concurrency control, logging and recovery, replication and high availability, scalability and cloud computing, and more. The goal of lectures and presentations is to understand classic techniques and to link them to industrial reality and to research opportunities. The target audience are recent MS graduates who have already taken 2-3 courses covering data management software and who are embarking on a career in industrial development or in academic research in related topics.

Veranstaltungen für Studenten der Informatik,    Informations & Wissensmanagement, Big Data Computing

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

The Autumn School for Information Retrieval and Information Foraging (ASIRF) focuses on recent developments in Information Retrieval and Information-Seeking behavior and their applications. Information Retrieval deals with searching in huge amounts of unstructured data. In many professional fields of work and everyday life, access to text data via search engines is one of the most important forms of access to knowledge. Information Foraging aims to model the entire process from the awareness of an information problem through selecting appropriate information sources to the actual search.

Students and lecturers will live together for a week at Schloss Dagstuhl, which guarantees the school's traditionally highly communicative and interactive atmosphere. The intended audience of ASIRF are doctoral students and young researchers working in Information Retrieval, Information Foraging, or using concepts or methods from these fields in their research

Veranstaltungen für Studenten der Informatik,    Informations & Wissensmanagement, Big Data Computing

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

Retrieval-augmented generation (RAG) has proven effective in conditioning the output of large language models (LLMs) on relevant documents and for grounding LLM-generated statements, this way combatting the so-called confabulation or hallucination problem. Basically, RAG combines (1) a retrieval phase, where a search system identifies relevant documents for a user prompt, and (2) a generation phase, where an LLM synthesizes a tailored answer, probably linking to the retrieved sources.