In: IEEE 37th International Conference on Data Engineering (ICDE), 2021, p. 2661--2664
Anomaly detection is a fundamental problem that consists of identifying irregular patterns that do not conform to the expected behavior of a system or the generated data. Many anomaly detection techniques have been proposed for time series data. However, selecting the most suitable detection method remains challenging as the proposed techniques widely vary in performance. The appropriate...
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In: Information Retrieval Journal, 2015, vol. 18, no. 5, p. 445-472
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In: WWW '20: Proceedings of The Web Conference 2020, 2020, vol. April, p. 1851-1862
Finding social influencers is a fundamental task in many online applications ranging from brand marketing to opinion mining. Existing methods heavily rely on the availability of expert labels, whose collection is usually a laborious process even for domain experts. Using open-ended questions, crowdsourcing provides a cost-effective way to find a large number of social influencers in a short...
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In: Proceedings of the 1st Wikidata Workshop (Wikidata 2020) co-located with 19th International Semantic Web Conference (ISWC 2020), Virtual Conference, November 2-6, 2020, 2020, vol. 2773, p. 1-15
Wikidata is a key resource for the provisioning of structured data on several Wikimedia projects, including Wikipedia. By design, all Wikipedia articles are linked to Wikidata entities; such mappings represent a substantial source of both semantic and structural information. However, only a small subgraph of Wikidata is mapped in that way – – only about 10% of the sitelinks are linked to...
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In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020, p. 2184-2190
Location prediction is a key problem in human mobility modeling, which predicts a user's next location based on historical user mobility traces. As a sequential prediction problem by nature, it has been recently studied using Recurrent Neural Networks (RNNs). Due to the sparsity of user mobility traces, existing techniques strive to improve RNNs by considering spatiotemporal contexts. The...
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In: CIDR 2020, 10th Conference on Innovative Data Systems Research, Amsterdam, The Netherlands, January 12-15, 2020, Online Proceedings, 2020, p. 1-8
In-memory databases rely on non-volatile storage devices for services such as durability and recovery. SSDs can provide the high-performance these services require. When performance problems occur, however, SSDs offer no mechanism to help analyze them. The only alternative is to instrument the database side of the problem and conjecture about what might be the cause of performance...
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In: Knowledge and Information Systems, 2020, vol. 62, p. 2257-2280
Missing values are very common in real-world data including time-series data. Failures in power, communication or storage can leave occasional blocks of data missing in multiple series, affecting not only real-time monitoring but also compromising the quality of data analysis. Traditional recovery (imputation) techniques often leverage the correlation across time series to recover missing...
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In: IEEE Data Engineering Bulletin, 2020, vol. 43, no. 1, p. 60-71
Many large computer clusters offer alternative computing elements in addition to general-purpose CPUs. GPU and FPGAs are very common choices. Two emerging technologies can further widen the options in that context: in-network computing (INC) and near-storage processing (NSP). These technologies support computing over data that is in transit between nodes or inside the storage stack,...
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In: The Web Conference 2021, Ljubljana, Slovenia, April 12-23, 2021, 2021, p. 1-10
Wikidata is rapidly emerging as a key resource for a multitude of online tasks such as Speech Recognition, Entity Linking, Question Answering, or Semantic Search. The value of Wikidata is directly linked to the rich information associated with each entity – that is, the properties describing each entity as well as the relationships to other entities. Despite the tremendous manual and...
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In: The Web Conference 2021, Ljubljana, Slovenia, April 12-23, 2021, 2021, p. 1-12
Knowledge Graph (KG) completion has been widely studied to tackle the incompleteness issue (i.e., missing facts) in modern KGs. A fact in a KG is represented as a triplet (ℎ, 𝑟, 𝑡 ) linking two entities ℎ and 𝑡 via a relation 𝑟 . Existing work mostly consider link prediction to solve this problem, i.e., given two elements of a triplet predicting the missing one, such as (ℎ,...
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