1.
low-latency.com을 자주 방문합니다. 한동안 재미있는 글들이 올라오지 않았습니다. Low Latency가 키워드였던 시대가 저물고 있다는 뜻이 아닐까요? low-latency.com이 분기별로 행사를 하는데 가을 주제가 “Big Fast Data for Automated Trading”입니다. 의미 심장하지 않으신가요?
Low-Latency: No Longer a Strategy. So What Is?
Outright speed is no longer enough, even if one can afford it. Intelligent trading, aka smart trading, is the new focus. It means making better trading decisions through data-driven analytics, and executing with competitive latency, at an affordable cost. What are the best approaches to participating in the intelligent trading marketplace?News and Social Media for Trading – Analytics over Latency?
Trading strategies driven at least in part by news and social media updates are being increasingly adopted, and generating investment returns that many are taking note of. What sources of information are suitable for what trading strategies, and what approaches are available for connecting to and processing this potentially lucrative Big Data world?In-Memory in The Real World – Your Competitors are Already There
RAM is up to 100,000 times faster than disk, so its use to minimize I/O latency for storage is well understood. Recent hardware/software advances have made in-memory computing more usable for large amounts of data, making it an approach that is relevant to both low-latency and big data processing. How is in-memory playing out for trading applications?
위의 글을 보고 아래를 포스팅했습니다.
해외 어떤 행사의 주제가 "Big Fast Data for Automated Trading"입니다. "많은 데이타를 빠르게 자동으로"가 앞으로의 경쟁력의 방향입니다. 이제 Low Latency는 기본입니다.
— smith Kim (@smallake) September 5, 2013
2.
무엇을 하든 통계분석능력이 중요할 듯 합니다.지난 빅데이타 발표자가 강조하였던 것도 “통계”였습니다. 그래서 준비했습니다. ” top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM)”이라는 논문입니다. 깊이가 필요하신 분들에게 도움이 되시길 바랍니다.
This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and reviewcurrent and further research on the algorithm. These 10 algorithms cover classification,clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.
