d) ・∑ (tf(t in d) ・idf(t)2 ・norm(t, d)・t.getBoost()) (t in q) Query normalization factor - Normalize query to compare with results from other queries Coordination factor - Reward documents with more individual query terms Term frequency – How often does the term appear in the doc? Inverse document frequency – How often does the term appear in all docs? Term boost Field length norm – How long is the field?
Short fields not auto-boosted • Tweakable parameters (beware) • Is BM25 better? ‒ Literature suggests so ‒ Challenges suggest so (TREC, ...) ‒ Users say so ‒ Lucene developers say so ‒ Konrad Beiske says so: Blog “BM25 vs Lucene Default Similarity” • But: It depends on the features of your corpus
Thermal mass for a single egg is 274 J / °C Integrated temperature from 4 to 80 C gives us total heat of: 274 J/C * (80 - 4 °C) D2 series uses Haswell Intel Xeon E5-2673v3 processors Thermal Design Power: 120W We used 8 cores of the 12 cores total for .75 * 120W * 135 Procs = 90W Total Query execution time in seconds: 83 min x 60 s 5 Total Energy = 12,150W * 4980 seconds (length of query) 274 J/°C 20,812 J 12,150 W 4,990 s 60.5 MJ
Quiet on the Digital Front: Security Analytics @ USAA OpenSource Connections: The Ghost in the Search Machine Grid Monitoring at CERN with the Elastic Stack Contributing to Elasticsearch: How to Get Started All recordings: https://www.elastic.co/elasticon/conf/2016/sf/