Online Change Estimation Models for Dynamic Web Resources

Horincar, Roxana; Amann, Bernd; Artières, Thierry

doi:10.1007/978-3-642-31753-8_33

Cited by 4 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In measurement literature, the majority of effort was spent on the behavior of web pages, including analysis of server logs [27], page-modification frequency during crawling [2], [4], [17], [24], RSS feed dynamics [34], and content change between consecutive observations [1], [14], [26]. Problems related to estimation of F U (x) have also emerged in prediction of future updates [5], [6], [13], [18], [31], [38], with a good survey in [25], and user lifetime measurement in decentralized P2P networks [3], [33], [37], [40].…”

Section: Related Workmentioning

confidence: 99%

Temporal update dynamics under blind sampling

Cline

Loguinov

2015

2015 IEEE Conference on Computer Communications (INFOCOM)

View full text Add to dashboard Cite

Abstract-Network applications commonly maintain local copies of remote data sources in order to provide caching, indexing, and data-mining services to their clients. Modeling performance of these systems and predicting future updates usually requires knowledge of the inter-update distribution at the source, which can only be estimated through blind sampling -periodic downloads and comparison against previous copies. In this paper, we first introduce a stochastic modeling framework for this problem, where the update and sampling processes are both renewal. We then show that all previous approaches are biased unless the observation rate tends to infinity or the update process is Poisson. To overcome these issues, we propose four new algorithms that achieve various levels of consistency, which depend on the amount of temporal information revealed by the source and capabilities of the download process.

show abstract

Section: Related Workmentioning

confidence: 99%

Temporal update dynamics under blind sampling

Cline

Loguinov

2015

2015 IEEE Conference on Computer Communications (INFOCOM)

View full text Add to dashboard Cite

show abstract

“…To tackle both content and score dynamicity, a first solution consists in extending a content retrieval model (textual, spatial) and implementing adequate index structures and refresh strategies for low-latency and high throughput snapshot query evaluation. Best-effort refresh strategies [7,16] could determine optimal re-evaluation of active user queries which combined with real-time content indexing [3,2,31,22,19] can achieve high result freshness and completeness. However, real-time content indexing systems usually accommodate high arrival rates of items at the expense of result accuracy by either (a) excluding a significant portion of the incoming items (e.g.…”

Section: Introductionmentioning

confidence: 99%

Continuous Top-k Queries over Real-Time Web Streams

Vouzoukidou¹,

Amann²,

Christophides³

2016

Preprint

Self Cite

View full text Add to dashboard Cite

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 information relevance, Real-time Web applications such as Twitter and Facebook, extend content and social-graph relevance scores with "real-time" user generated events (e.g. re-tweets, replies, likes). To accommodate high arrival rates of information items and user events we explore a publish/subscribe paradigm in which we index queries and update on the fly their results each time a new item and relevant events arrive. In this setting, we need to process continuous top-k text queries combining both static and dynamic scores. To the best of our knowledge, this is the first work addressing how non-predictable, dynamic scores can be handled in a continuous top-k query setting.

show abstract