Titlebook: Database and Expert Systems Applications; 30th International C Sven Hartmann,Josef Küng,Ismail Khalil Conference proceedings 2019 Springer

伪造者

Succinct BWT-Based Sequence Predictionams. Sequence prediction is a popular task, which consists of predicting the next symbol of a sequence, given a set of training sequences. Although numerous prediction models have been proposed, many have a low accuracy because they are lossy models (they discard information from training sequences

adroit

TRR: Reducing Crowdsourcing Task Redundancyd to accomplish a crowdsourcing task. Typical task assignment systems assign tasks to a fixed number of crowd workers, while tasks are varied in difficulty as being easy or hard tasks. Easy tasks need fewer task assignments than hard tasks. We present TRR, a task redundancy reducer that assigns task

充满装饰

evasive

Feature

恭维

Querying in a Workload-Aware Triplestore Based on NoSQL DatabasesL querying has been the main issue for virtually all the recent RDF triplestores. This paper presents ., a middleware that addresses workload-adaptive management of large RDF graphs. Our middleware not only employs all the most used NoSQL data models but also provides a novel RDF data partitioning a

constitute

Vo2-Max

PFed: Recommending Plausible Federated SPARQL Querieswing number of datasets is challenging. In this paper, we propose ., an approach to recommend plausible federated queries based on real query logs of different datasets. The problem is not to find similar federated queries, but plausible complementary queries over different datasets. Starting with a

FELON

Representing and Reasoning About Precise and Imprecise Time Points and Intervals in Semantic Web: De number of approaches exist. However, they handle only precise temporal data and lack imprecise ones. In this paper, we propose a crisp-based approach for representing and reasoning about temporal data in term of quantitative (i.e., time points that can be dates and clocks, and time intervals) as we

diathermy

Database and Expert Systems Applications978-3-030-27618-8Series ISSN 0302-9743 Series E-ISSN 1611-3349