Research on Algorithm for Data Digging Online
نویسندگان
چکیده
منابع مشابه
Research on Algorithm Recommended by Online Education for Big Data
“Big data” is becoming a hot topic in the Internet. The long tail problem of the massive online courses also becomes the biggest headache for operation team of online education. The manner in which the reader wants most courses show to be presented before the user is the key to improve the quality of online education. Personalized recommendation system is to discover the readers interests tende...
متن کاملDigging for Data Structures
Because writing computer programs is hard, computer programmers are taught to use encapsulation and modularity to hide complexity and reduce the potential for errors. Their programs will have a high-level, hierarchical structure that reflects their choice of internal abstractions. We designed and forged a system, Laika, that detects this structure in memory using Bayesian unsupervised learning....
متن کاملOnline EM Algorithm for Latent Data Models
In this contribution, we propose a generic online (also sometimes called adaptive or recursive) version of the Expectation-Maximisation (EM) algorithm applicable to latent variable models of independent observations. Compared to the algorithm of Titterington (1984), this approach is more directly connected to the usual EM algorithm and does not rely on integration with respect to the complete d...
متن کاملOnline Data Collection for Psychotherapy Process Research
A preliminary investigation addressed the feasibility of using a specially designed online database to collect psychotherapy session impact and therapist-client alliance data and compared these online measures to published results of their paper-and-pencil counterparts. Participants drawn from a psychology department clinic, a student counseling center, and community agencies visited an online ...
متن کاملData Diffusion Delivers Dynamic Digging
We want to support interactive analysis (“digging”) of large quantities of data, a requirement that arises, for example, in many scientific disciplines. Such analyses require turnaround measured in minutes or seconds. Achieving this performance can demand hundreds of computers to process what may be many terabytes of data. As the applications scale, data sets grow, and resources used increase, ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Energy Procedia
سال: 2011
ISSN: 1876-6102
DOI: 10.1016/j.egypro.2011.12.302