Random Document Sampling via Time Window Queries
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Solution Overview
Problem
Existing methods for random sampling from a search engine, such as those described by Bar-Yossef and Gurevich, favor longer documents and are biased towards documents with high static ranks, especially when dealing with large corpora and limited result sets, making it costly and inefficient to obtain a true random sample.
Innovation Solution
The method involves assigning time-stamps to documents and selecting random time windows within a timeframe of interest, sending search queries to a search engine to retrieve documents within these windows, and adjusting the window size to ensure a predefined number of documents are included, allowing for unbiased sampling independent of document content or size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If random queries are formulated using term frequencies from a lexicon, then documents can be sampled from the corpus, but longer documents are favored because they match more queries
Solution Approach 1:
The patent changes the parameter basis for query formulation from term frequencies to time-based parameters. Instead of using lexicon terms and their frequencies to construct random queries, the system uses randomly selected time windows to retrieve documents, fundamentally altering the sampling parameter space to eliminate content-based bias
Solution Approach 2:
The patent inverts the traditional sampling approach by not querying for specific document content but rather querying for documents within specific time periods. This inversion transforms the sampling mechanism from content-driven to time-driven, ensuring uniform selection probability across all documents
2Productivity
If the search engine returns only top N results, then the sampling process is limited by the result set size, but documents with high static rank are biased towards
Solution Approach 1:
The patent applies preliminary action by pre-dividing the total time period into multiple time windows before sampling. This pre-structure ensures that when queries are executed, the time-based buckets are already defined, allowing the system to work within the N result limit while maintaining randomness through the pre-established time divisions
Solution Approach 2:
The patent introduces time windows as an intermediary between the query system and the document corpus. These time-based buckets act as mediators that translate random sampling requirements into structured time-based queries, enabling uniform document selection even when constrained by the top N results limitation
3Quantity of substance
If more random terms are used in conjunctive queries to reduce results to less than N, then the bias towards longer documents increases
Solution Approach 1:
The patent inverts the approach to controlling result quantity by not using more terms to reduce results, but rather by using time window constraints to naturally limit results. This inversion eliminates the need to balance term count against document length bias, as the time-based approach inherently provides uniform sampling
4Measurement precision
If millions of search interface invocations are made to get a random sample from a multi-lingual large corpus, then the sample can be obtained, but the process becomes expensive
Solution Approach 1:
The patent applies preliminary action by pre-dividing the total time period into multiple time windows before sampling. This pre-structure ensures that when queries are executed, the time-based buckets are already defined, allowing the system to work within the N result limit while maintaining randomness through the pre-established time divisions
Solution Approach 2:
The patent changes the parameter basis for query formulation from term frequencies to time-based parameters. Instead of using lexicon terms and their frequencies to construct random queries, the system uses randomly selected time windows to retrieve documents, fundamentally altering the sampling parameter space to eliminate content-based bias
Data Source
AI summary
A method for providing one or more random sample documents from a corpus of documents using a search engine is provided. The providing of each of the random sample documents comprises selecting randomly a time window from a set of time windows. A search query is sent to the search engine defining a search for documents of the corpus with time-stamps within the time window defined by the randomly selected time window. In response to the sending of the search query, a search result is receiving from the search engine. The search result comprises a set of the documents of the corpus with time-stamps within the time window. One of the documents comprised by the received set of documents is then selected randomly.