Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Amann, Bernd"'
This paper presents ATEM, a novel framework for studying topic evolution in scientific archives. ATEM is based on dynamic topic modeling and dynamic graph embedding techniques that explore the dynamics of content and citations of documents within a s
Externí odkaz:
http://arxiv.org/abs/2306.02221
Autor:
Rahimi, Hamed, Hoover, Jacob Louis, Mimno, David, Naacke, Hubert, Constantin, Camelia, Amann, Bernd
The recent explosion in work on neural topic modeling has been criticized for optimizing automated topic evaluation metrics at the expense of actual meaningful topic identification. But human annotation remains expensive and time-consuming. We propos
Externí odkaz:
http://arxiv.org/abs/2305.14587
This paper presents an algorithmic family of dynamic topic models called Aligned Neural Topic Models (ANTM), which combine novel data mining algorithms to provide a modular framework for discovering evolving topics. ANTM maintains the temporal contin
Externí odkaz:
http://arxiv.org/abs/2302.01501
We present a comprehensive set of conditions and rules to control the correctness of aggregation queries within an interactive data analysis session. The goal is to extend self-service data preparation and BI tools to automatically detect semanticall
Externí odkaz:
http://arxiv.org/abs/2111.13927
The vast amount of processing power and memory bandwidth provided by modern Graphics Processing Units (GPUs) make them a platform for data-intensive applications. The database community identified GPUs as effective co-processors for data processing.
Externí odkaz:
http://arxiv.org/abs/1907.00050
Publikováno v:
In Big Data Research 15 November 2021 26
The Web has become a large-scale real-time information system forcing us to revise both how to effectively assess relevance of information for a user and how to efficiently implement information retrieval and dissemination functionality. To increase
Externí odkaz:
http://arxiv.org/abs/1610.06500
The number of linked data sources and the size of the linked open data graph keep growing every day. As a consequence, semantic RDF services are more and more confronted to various "big data" problems. Query processing is one of them and needs to be
Externí odkaz:
http://arxiv.org/abs/1604.08903
The number of linked data sources and the size of the linked open data graph keep growing every day. As a consequence, semantic RDF services are more and more confronted with various "big data" problems. Query processing in the presence of inferences
Externí odkaz:
http://arxiv.org/abs/1510.03409
Querying very large RDF data sets in an efficient manner requires a sophisticated distribution strategy. Several innovative solutions have recently been proposed for optimizing data distribution with predefined query workloads. This paper presents an
Externí odkaz:
http://arxiv.org/abs/1507.02321