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Internet Search Engine freshness by Web Server help Presented by: Barilari Alessandro

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Mining di Dati WebAlessandro Barilari 2 Introduction Search engines are an important source of information and keeping them up-to-date will result in more accurate answers to search queries. Search engines create their databases by probing web servers on a per-URL basis with a little help from the web servers.

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Mining di Dati WebAlessandro Barilari 3 Main Problem There are no standard for facilitating the push of updates from servers to search engines: – It takes up to six months for a few page to be indexed by popular web search engines; – The data which is indexed by the search engines is often stale.

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Mining di Dati WebAlessandro Barilari 4 Solution… Web server help to facilitate search engine freshness results in a favorable situation for web sites, search engines and users.

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Mining di Dati WebAlessandro Barilari 5 …and its problems The number of updates per second is very large. Must balance between: – The number of interactions between web sites and search engines, and – The freshness of the search engines.

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Mining di Dati WebAlessandro Barilari 6 Page rank impact Pages which are popular will have higher page ranks: – Use popularity in addition to age and freshness to compute the mismatch between a web site and a search engine

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Mining di Dati WebAlessandro Barilari 7 Summary Definitions and Cost Model Algorithm Analysis Pratical issues

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Mining di Dati WebAlessandro Barilari 8 Some definitions Update: an update u to a file f is a modification to f that has been flushed to the disk; Propagation of an update: an update is said to be propagate when the web site has informed the search engine about the update. A SE may or may not retrieve that update; Meta-update propagation: At any time t, let U(t) be the set of unpropagated updates. The web site informs the search engine about all the updates U(t);

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Mining di Dati WebAlessandro Barilari 9 Some definitions (2) Weight of a file: given a content file, its weight f (non- negative) denotes the importance of the file; the weights are chosen such that: Last_modification_time(u,t): the last time before t when the file f(u) was updated.

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Mining di Dati WebAlessandro Barilari 10 The Cost Model Components: – Communication cost; – Opportunity cost: represents the stalenes of the search engine data as compared to the data on the web server. CPU cost is ignored

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Mining di Dati WebAlessandro Barilari 11 Opportunity cost (OC) Given an unpropagated update u to a content file f; the opportunity cost for update u at time t is: OC(u,t)= f(u) x(t - last_modification_time(u,t)) Definition for meta-update propagation:

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Mining di Dati WebAlessandro Barilari 12 Communication cost (CC) size f(u) (t): the size of file f(u) at time t;

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Mining di Dati WebAlessandro Barilari 13 Potential Communication cost (PCC) Represents the communication cost which would need to be incurred in case update u were to be propagated after time t:

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Mining di Dati WebAlessandro Barilari 14 The Cost Function Given that an update u is unpropagated at time t, the cost function for that update at time t is given by:

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Mining di Dati WebAlessandro Barilari 15 Summary Definition and Cost Model Algorithm Analysis Pratical issues

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Mining di Dati WebAlessandro Barilari 16 FreshFlow Algorithm When OC_tot equals PCC_tot at any time t, the web server can inform the search engine about all the unpropagated updates.

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Mining di Dati WebAlessandro Barilari 17 Summary Definition and Cost Model Algorithm Analysis Pratical issues

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Mining di Dati WebAlessandro Barilari 18 Analysis The cost of the FreshFlow algorithm (called FF) is compared with the cost of an optimal off-line algorithm (called ADV)

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Mining di Dati WebAlessandro Barilari 19 Analysis (2) Lemma (1): OC(u,t) is monotonically non- decreasing; Lemma (2): suppose an update u to a file f, and suppose FF transmits but ADV does not. Then OC ADV (u,t)OC FF (u,t). Lemma (3): if the update is transmitted by the adversary (ADV), then CC ADV (u,t) CC FF (u,t).

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Mining di Dati WebAlessandro Barilari 20 Theorem FF is 2-competitive: Cost FF (u,t) 2 x Cost ADV (u,t)

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Mining di Dati WebAlessandro Barilari 21 Summary Definition and Cost Model Algorithm Analysis Pratical issues

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Mining di Dati WebAlessandro Barilari 22 Pratical issues There are multiple search engines: – Synchronization effect: pushing the updates would put pressure on the last-hop link to the web server; – Search engine load: some search engines might deny the receipt of updates.

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Mining di Dati WebAlessandro Barilari 23 The middleman approach Each web server contacts only one middleman for sending its updates; Could be a group of middlemen.

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Mining di Dati WebAlessandro Barilari 24 Benefits The middleman can solve some additional issues: – Verifying trustworthiness of web servers; – Restricting the rate at which updates get transmitted to search engines;

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Mining di Dati WebAlessandro Barilari 25 Limitations The algorithm has not been used in practice; The search engines need the cooperation of the web servers to keep track of updates to their URLs. Whether web servers will incorporate such a service remains to be seen.

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Mining di Dati WebAlessandro Barilari 26 Conclusions The FreshFlow algorithm is a solution that improve the data updates of the search engines, mantaining high level efficiency and performance; The authors are planning to implement the algorithm in a real system (and have a future pubblication!)

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