Danila Matveev optician

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

	class WebSocketActor(out: ActorRef) extends Actor {

	def receive = {
	case msg: JsValue => distributeCommands(msg)
	}

	def distributeCommands(req: JsValue): Unit = {
	(req \ "command").asOpt[String] match {
	case Some("get all accounts") => mock.getAllAccounts pipeTo out
	case Some("add account") => mock.addAccount pipeTo out

	case Event(newConnection: ActorRef, FSMData.Datum(challengeRef, channels, state)) =>
	context.watch(newConnection)
	newConnection ! CurrentState(stateName).json
	stay() using FSMData.Datum(challengeRef, channels + newConnection, state)

	val f = scala.io.Source.fromFile("jm.log")

	def trunk(value: Double): Double = BigDecimal(value).setScale(2, BigDecimal.RoundingMode.HALF_UP).toDouble

	def percentile(ms: Map[Long, Long], amount: Long, lvls: List[Int]): List[Long] = {
	val ls = ms.toList.sortBy(x => x._1)
	val lvlValues = lvls map (lvl => Math.ceil(amount*lvl/100f).toLong)
	def search(a: Long, xs: List[(Long, Long)], lvlValue: Long): Long = xs match {
	case v::xs if a+v._2 < lvlValue => search(a+v._2, xs, lvlValue)
	case v::xs => v._1

	val f = scala.io.Source.fromFile("jm.log")

	def trunk(value: Double): Double = BigDecimal(value).setScale(2, BigDecimal.RoundingMode.HALF_UP).toDouble

	def percentile(ms: Map[Long, Long], amount: Long, lvls: List[Int]): List[Long] = {
	val ls = ms.toList.sortBy(x => x._1)
	val lvlValues = lvls map (lvl => Math.ceil(amount*lvl/100f).toLong)
	def search(a: Long, xs: List[(Long, Long)], lvlValue: Long): Long = xs match {
	case v::xs if a+v._2 < lvlValue => search(a+v._2, xs, lvlValue)
	case v::xs => v._1

	(Source.single(request -> 42) via Http().superPool() map {
	case (Success(res), flag) => res.entity.toStrict(1 seconds).map(x => new String(x.data.toArray))
	case (Failure(err), flag) => Future.failed(err)
	}).mapAsyncUnordered(2)(???)

	import org.scalameter._

	import scala.concurrent.ExecutionContext.Implicits.global
	import scala.concurrent.duration.Duration
	import scala.concurrent.{Await, ExecutionContext, Future}


	object Main {

	/*

	import pushka.json._
	import pushka.annotation._

	// First case.
	// Simple as is.
	@pushka
	case class A(subModel: B, i: Int, str: String)

	@pushka
	case class B(field1: Int, field2: List[Int])

	import scala.io._
	import java.io._
	import scala.collection.mutable.ArrayBuffer

	object Main {
	def main(args: Array[String]): Unit = {
	var i = 0
	var v1: ArrayBuffer[String] = new ArrayBuffer(10000)
	var v2: ArrayBuffer[String] = new ArrayBuffer(10000)
	Source.stdin.getLines.foreach{

	class A{
	val x: PartialFunction[Any, Int] = {
	case Some(x: Int) => x
	}
	}