Organic market data: Bridging the gap between data collectors and end users
نویسندگان
چکیده
منابع مشابه
Bridging the Gap between Linked Data and the Semantic Desktop
The exponential growth of the World Wide Web in the last decade brought an explosion in the information space, which has important consequences also in the area of scientific research. Finding relevant work in a particular field and exploring the links between publications is currently a cumbersome task. Similarly, on the desktop, managing the publications acquired over time can represent a rea...
متن کاملBridging the Knowledge Gap between Transactional Databases and Data Warehouses
Data warehouse is widely recognized in the industry as the principal decision support system architecture and an integral part of the corporate information system. However, the majority of academic institutions in the US and world-wide have been slow in developing curriculums that reflect this reality. This paper examines the issues that have contributed to the lag in the coverage of data wareh...
متن کاملBridging the Gap Between Synthetic and Real Data
There is a long tradition of using generative models in combination with discriminative classifiers [5, 6, 7]. Equally the recently successful deep learning technique [3] use jittering techniques [1, 2] that imply sampling from an underlying distribution. Although in both cases the the model is postulated and all parameters are in our control, we rarely achieve an accurate representation of the...
متن کاملBridging the Data Management Gap Between Service and Desktop Grids
Volunteer computing platforms have become a popular means of providing vast amounts of processing power to scientific applications through the use of personal home computers. To date, with little exception, these systems have focused solely on exploiting idle CPU cycles and have yet to take full advantage of other available resources such as powerful video card processors, hard disk storage cap...
متن کاملBridging the Gap between HPC and Big Data frameworks
Apache Spark is a popular framework for data analytics with attractive features such as fault tolerance and interoperability with the Hadoop ecosystem. Unfortunately, many analytics operations in Spark are an order of magnitude or more slower compared to native implementations written with high performance computing tools such as MPI. There is a need to bridge the performance gap while retainin...
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: Acta fytotechnica et zootechnica
سال: 2015
ISSN: 1336-9245
DOI: 10.15414/afz.2015.18.si.148-150