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- Online Topic-Aware Influence Maximizati - VLDB
pread by taking into consideration topics To address this problem, we study topic-aware in uence maxi-mization, which, given a topic-aware in uence maximization (TIM) query, nds k seeds from a social network such that the topic-aware i
- Historian Database Reference - Logic Control
Welcome This guide describes the database model of the AVEVA Historian system Each database entity is described, and the relationships between the entities are defined It is very important that you understand these data structures and relationships to effectively query AVEVA Historian and build productive client applications that interact with it
- thesis_v18 - Charlotte
The first study of generative topic models used for representation in information retrieval We investigate a range of topic models, especially generative topic models, in different manners of text representation in the language modeling framework Retrieval effectiveness is evaluated and compared The first evaluation of LDA-style topic models with very large text collections We evaluate LDA
- Topic-aware Most Influential Community Search in Social Networks
In this paper, we study a new problem of topic-aware most influ-ential community search in social networks (TAMICS, for short) Specifically, given a social network and a query topic distribution, the social network will transform into diferent directed uncertain graphs based on the query topic distribution
- Comparative Analysis of Topic Modeling on People Query- Based . . .
deas about how query terms and URLs are related in a thorough way The results of the experiment show that topic modeling is a great way to figure out what query logs really mean When it comes to useful applications and quantitative m
- A Query-Driven Topic Model - arXiv. org
A possible solution is to incorporate domain-specific knowledge into topic modeling, but this requires a spec-ification from domain experts We propose a novel query-driven topic model that allows users to specify a simple query in words or phrases and return query-related topics, thus avoiding tedious work from domain experts
- Improved Query Topic Models via Pseudo-Relevant P ́olya . . .
Abstract Query-expansion via pseudo-relevance feedback is a popular method of overcoming the problem of vocabulary mismatch and of in-creasing average retrieval effectiveness In this paper, we develop a new method that estimates a query topic model from a set of pseudo-relevant documents using a new language modelling framework
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