- Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
- Models and Issues in Data Stream Systems
- Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
- Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
- [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t
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#!/usr/bin/python3 | |
from itertools import product | |
T = int(input()) | |
for t in range(T): | |
N, M = list(map(int, input().split())) | |
grid = [list(map(int, input().split())) for n in range(N)] | |
rowmax = list(map(max, grid)) |
- Get evaluation license key : http://my.datomic.com/eval/request
- Download pro version : http://downloads.datomic.com/pro.html
- Dev mode setup
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(ns friend-oauth | |
(:require | |
[cemerick.friend :as friend] | |
[cemerick.friend.workflows :refer [make-auth]] | |
[clj-http.client :as client] | |
[oauth.client :as oauth] | |
[ring.util.request :as request])) | |
(defn- which-oauth-uri | |
[config request] |
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(ns org.flausenhaus.rocksdb | |
"Idiomatic Clojure bindings for the C++ RocksDB library, an embedded | |
persistent key-value store for flash storage and server workloads based on | |
LevelDB. More details about RocksDB are given at | |
https://github.com/facebook/rocksdb/wiki/Rocksdb-Architecture-Guide. | |
The implementation here borrows heavily from Factual's clj-leveldb. | |
This namespace provides a few things: | |
0. Protocols/type definitions: byte serialization (IByteSerializable), | |
closeable sequences (CloseableSeq), and mutable |
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(require 'dbg) | |
(global-set-key (kbd "<C-S-f5>") 'dbg-restart) | |
(global-set-key (kbd "<f5>") 'dbg-continue) | |
(global-set-key (kbd "<f9>") 'dbg-toggle-breakpoint) | |
(global-set-key (kbd "<f8>") 'dbg-watch) | |
(global-set-key (kbd "<f10>") 'dbg-next) | |
(global-set-key (kbd "<C-f10>") 'dbg-continue-to-here) | |
(global-set-key (kbd "<f11>") 'dbg-step) | |
(global-set-key (kbd "<C-S-f10>") 'dbg-jump) |
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; 1. rename attribute: :url/path → :legacy/url-path-v1 | |
(do (require '[datomic.api :as datomic] '[kanasubs.db :as db]) | |
(datomic/transact @db/connection | |
[{:db/id :url/path, :db/ident :legacy/url-path-v1}])) | |
; 2. repurpose :url/path | |
(do (in-ns 'kanasubs.db) | |
(transact @connection | |
[{:db/id #db/id[:db.part/db] | |
:db/ident :url/path |
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(ns green-eggs.core | |
(:require [clojure.core.async | |
:as a | |
:refer [>! <! >!! <!! go chan buffer close! thread | |
alts! alts!! timeout onto-chan]] | |
[clj-time.core :as t] | |
[clj-time.coerce :refer [from-date]])) | |
;If you were to implement yourself, you could make a `go` that simply pulls from a channel, and adds to another as a | |
;`[timestamp item]` pair, then finally pushes into an unbounded `chan` that has a transducer that filters based on |
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