Thursday, November 16, 2017

2017-11-16: Paper Summary for Routing Memento Requests Using Binary Classifiers

While researching my dissertation topic, I re-encountered the paper, "Routing Memento Requests Using Binary Classifiers" by Bornand, Balakireva, and Van de Sompel from JCDL 2016 (arXiv:1606.09136v1). The high-level gist of this paper is that by using two corpora of URI-Rs consisting of requests to their Memento aggregator (one for training, the other for training evaluation), the authors were able to significantly mitigate wasted requests to archives that contained no mementos for a requested URI-R.

For each of the 17 Web archives included in the experiment, with the exception of the Internet Archive on the assumption that a positive result would always be returned, a classifier was generated. The classifiers informed the decision of, given a URI-R, whether the respective Web archive should be queried.

Optimization of this sort has been performed before. For example, AlSum et al. from TPDL 2013 (trip report, IJDL 2014, and arXiv) created profiles for 12 Web archives based on TLD and showed that it is possible to obtain a complete TimeMap for 84% of the URI-Rs requested using only the top 3 archives. In two separate papers from TPDL 2015 (trip report) then TPDL 2016 (trip report), Alam et al. (2015, 2016) described making routing decisions when you have the archive's CDX information and when you have to use the archive's query interface to expose its holdings (respectively) to optimize queries.

The training data set was based off of the LANL Memento Aggregator cache from September 2015 containing over 1.2 million URI-Rs. The authors used Receiver Operating Characteristic (ROC) curves comparing the rate of false positives (URI-R should not have been included but was) to the rate of true positives (URI-R was rightfully included in the classification). When requesting a prediction from the classifier once training, a pair of each of these rates is chosen corresponding to the most the most acceptable compromise for the application.

A sample ROC curve (from the paper) to visualize memento requests to an archive.

Classification of this sort required feature selection, of which the authors used character length of the URI-R and the count of special characters as well as the Public Suffix List domain as a feature (cf. AlSum et al.'s use of TLD as a primary feature). The rationale in choosing PSL over TLD was because of most archiving covering the same popular TLDs. An additional token feature was used by parsing the URI-R, removing delimiters to form tokens, and transforming the tokens to lowercase.

The authors used four different methods to evaluating the ranking of the features being explored for the classifiers: frequency over the training set, sum of the differences between feature frequencies for a URI-R and the aforementioned method, Entropy as defined by Hastie et al. (2009), and the Gini impurity (see Breiman et al. 1984). Each metric was evaluated to determine how it affected the prediction by training a binary classifier using the logistic regression algorithm.

The paper includes applications of the above plots for each of the four feature selection strategies. Following the training, they evaluated the performance of each algorithm, with a preference toward low computation load and memory usage, for classification using correspond sets of selected features. The algorithms evaluated were logistical regression (as used before, Multinomial Bayes, Random Forest, and SVM. Aside from Random Forest, the other three algorithms had similar runtime predictions, so were evaluated further.

A classifier was trained using each permutation of the three remaining algorithms and each archive. To determine the true positive threshold, they brought in the second data set consisting of 100,000 unrelated URI-Rs from the Internet Archive's log files from early 2012. Of the three algorithms, they found that logistic regression performed the best for 10 archives and Multinomial Bayes for 6 others (per above, IA was excluded).

The authors then evaluated the trained classifiers using yet another dataset of URI-Rs from 200,000 randomly selected requests (cleaned to just over 187,000) from Given the data set was based on inter-archive requests, it was more representative of that of an aggregator's requests compared to the IA dataset. They computed recall, computational cost, and response time using a simulated method to prevent the need for thousands of requests. These results confirmed that the currently used heuristic of querying all archives has the best recall (results are comprehensive) but response time could be drastically reduced using a classifier. With a reduction in recall of 0.153, less than 4 requests instead of 17 would reduce the response time from just over 3.7 seconds to about 2.2 seconds. Additional details of optimization obtained through evaluation of the true positive rate can be had in the paper.

Take Away

I found this paper to be an interesting an informative read on a very niche topic that is hyper-relevant to my dissertation topic. I foresee a potential chance to optimize archival query from other Memento aggregators like MemGator and look forward to further studies is this realm on both optimization and caching.

Mat (@machawk1)

Nicolas J. Bornand, Lyudmila Balakireva, and Herbert Van de Sompel. "Routing Memento Requests Using Binary Classifiers," In Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries (JCDL), pp. 63-72, (Also at arXiv:1606.09136).

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