相关论文: Technical Report: CSVM format for scientific tabul…

The CSVM format is derived from CSV format and allows the storage of tabular like data with a limited but extensible amount of metadata. This approach could help computer scientists because all information needed to uses subsequently the…

CSVM (CSV with Metadata) is a simple file format for tabular data. The possible application domain is the same as typical spreadsheets files, but CSVM is well suited for long term storage and the inter-conversion of RAW data. CSVM embeds…

CSV is a widely used format for data representing systems control, information exchange and processing, logging, etc. Nevertheless, the format is riddled with tricky corner cases and inconsistencies, which can make input data unreliable,…

Scientific applications produce a huge amount of data, which imposes serious management and analysis challenges. In particular, limitations in current database management systems prevent their adoption in simulation applications, in which…

Support Vector Machines (SVMs) are popular tools for data mining tasks such as classification, regression, and density estimation. However, original SVM (C-SVM) only considers local information of data points on or over the margin.…

When working with astronomical data, metadata is also important. A general-purpose file format for transmission, processing and archiving large datasets should facilitate, among other things, both efficient processing of bulk data and…

In this paper, we introduce a scientific format for text-based data files, which facilitates storing and communicating tabular data sets. The so-called Full-Metadata Format builds on the widely used INI-standard and is based on four…

Getting the best performance from the ever-increasing number of hardware platforms has been a recurring challenge for data processing systems. In recent years, the advent of data science with its increasingly numerous and complex types of…

We describe the current state and future plans for a set of tools for scientific data management (SDM) designed to support scientific transparency and reproducible research. SDM has been in active use at our MRI Center for more than two…

It is well known that data scientists spend the majority of their time on preparing data for analysis. One of the first steps in this preparation phase is to load the data from the raw storage format. Comma-separated value (CSV) files are a…

Data tables in the form of spreadsheets or delimited text files are the most utilised data format in Systems Biology. However, they are often not sufficiently structured and lack clear naming conventions that would be required for…

The support vector machine (SVM) and deep learning (e.g., convolutional neural networks (CNNs)) are the two most famous algorithms in small and big data, respectively. Nonetheless, smaller datasets may be very important, costly, and not…

Tabular data comprising rows (samples) with the same set of columns (attributes, is one of the most widely used data-type among various industries, including financial services, health care, research, retail, and logistics, to name a few.…

The Collaborative Analysis Versioning Environment System (CAVES) project concentrates on the interactions between users performing data and/or computing intensive analyses on large data sets, as encountered in many contemporary scientific…

Exchanging data as character-separated values (CSV) is slow, cumbersome and error-prone. Especially for time-series data, which is common in Activity Recognition, synchronizing several independently recorded sensors is challenging. Adding…

Support vector machine (SVM) is one of the most studied paradigms in the realm of machine learning for classification and regression problems. It relies on vectorized input data. However, a significant portion of the real-world data exists…

Representing scientific data sets efficiently on external storage usually involves converting them to a byte string representation using specialized reader/writer routines. The resulting storage files are frequently difficult to interpret…

Open data is an important basis for open science and evidence-based policymaking. Governments of many countries disclose government-related statistics as open data. Some of these data are provided as CSV files. However, since CSV files are…

Raw data sizes are growing and proliferating in scientific research, driven by the success of data-hungry computational methods, such as machine learning. The preponderance of proprietary and shoehorned data formats make computations slower…

Nowadays simulations can produce petabytes of data to be stored in parallel filesystems or large-scale databases. This data is accessed over the course of decades often by thousands of analysts and scientists. However, storing these volumes…