Object similarity with office laptop
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This document discusses using locality-sensitive hashing and minhashing to efficiently perform similarity searches on big medical record datasets. It proposes pre-computing clusters of similar patients and storing them in separate files, along with centroid files. To find similar patients, it loads a patient, reduces their dimensionality, compares them to centroids, loads the closest cluster, and compares them to patients within that cluster. This approach allows performing similarity searches in about 100 milliseconds using only 20GB of storage and 20MB of RAM on a typical laptop, providing an efficient solution for big data similarity searches.
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We talk a lot these days about data science, and how it will pave our paths with beautiful insights and unexpected new relations and connections in our given datasets, and even across datasets.
But how to maintain the "Science" part in "Data Science"? After some time working in this field I appreciate more and more the critical thinking which has characterized the progress in science.
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USF硕士毕业证成绩单【微信95270640】一比一伪造旧金山大学文凭@假冒USF毕业证成绩单+Q微信95270640办理USF学位证书@仿造USF毕业文凭证书@购买旧金山大学毕业证成绩单USF真实使馆认证/真实留信认证回国人员证明
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二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站永久可查
四：留信认证留学生信息网站永久可查
国外毕业证学位证成绩单办理方法：
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3毕业证成绩单电子版做好以后发送给您确认；
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Object similarity with office laptop
- 1. Efficient Similarity Search on Big Data with office laptop Sergii Shelpuk Head of Data Science, V.I.Tech
- 2. The Problem You have a database of 30M patients with all medical records. Each patient described by 250K of binary features. You need a system for finding N most similar patients to a given one. Jesus Christ, it’s Big Data, get Hadoop!
- 3. Jesus Christ, it’s Big Data, get Hadoop! Pre-compute none Pre-compute all 450+ trillion pairs Stored as key- values, more than 1Pb for values only Compare 30 million pairs by 250K features 37+ Tflops One Intel i7 would compute it in 10 minutes (pure computing time)
- 4. Can we do better? Two main ideas: - we don’t need the meaning of each feature, we only care about similarity of the patients; - we don’t want to compare very different patients, we want to compare only the most similar ones.
- 5. Step 1: Reduce dimensionality Decrease dimensionality of the data while preserving similarities Locality-sensitive hashing and minhashing
- 6. K-Means clustering K-Means clustering groups similar patients in one group
- 7. Step 2: Group similar Group similar patients and store groups as separate files Store centroids of each cluster in a separate file, too cluster1.bin clusterN.bin
- 8. Approach To find N similar patients: 1. Load a patient 2. Reduce dimensionality with minhashing 3. Load centroid file 4. Compare patient to every centroid 5. Load cluster file of the closest centroid 6. Compare patient with patients in the cluster 7. Show top N similar
- 9. Results 50000 clusters up to ~1000 patients per cluster ~500Kb-1Mb of every cluster file ~18Mb centroid file To do similarity search you need: ~20Gb HDD ~20Mb RAM Search works in ~100 milliseconds on a regular office laptop
- 10. Thank you