Effective data management is among key constituents of the
performance of a data analytics application. For Intel® oneAPI Data Analytics Library, effective data
management requires effectively performing the following operations:
Raw data acquisition, filtering, and normalization with data
Data conversion to a numeric representation for numeric tables.
Data streaming from a numeric table to an algorithm.
Depending on the usage model, you may also want to apply compression
and decompression to the data you operate on. You can either use
compression and decompression embedded into data source interfaces or
apply data serialization and deserialization interfaces.
oneDAL provides a set of customizable interfaces to operate on
your out-of-memory and in-memory data in different usage scenarios,
which include batch processing, online processing, and distributed
processing, as well as more complex scenarios, such as a combination
of online and distributed processing.
One of key concepts of Data Management in oneDAL is a data set.
is a collection of data of a defined structure that
characterizes an analyzed and modeled object. Specifically, the
object is characterized by a set of attributes (Features), which
form a Feature Vector of dimension p. Multiple feature vectors form
a set of Observations of size n. oneDAL defines a tabular view
of a data set where table rows represent observations and columns
An observation corresponds to a particular measurement of an observed
object, and therefore when measurements are done, at distinct moments
in time, the set of observations characterizes how the object evolves
It is not a rare situation when only a subset of features can be
measured at a given moment. In this case, the non-measured features
in the feature vector become blank, or missing. Special statistical
techniques enable recovery (emulation) of missing values.
You normally start working with oneDAL by selecting an
appropriate data source, which provides an interface for your raw
data set. oneDAL data sources support categorical, ordinal, and
continuous features. It means that data sources can automatically
transform non-numeric categorical and ordinary data into a numeric
representation. When the structure of your raw data is more complex
or when the default transformation mechanism does not fit your needs,
you may customize the data source by implementing a custom derivative
Because a data source is typically associated with out-of-memory
data, such as files, databases, and so on, streaming out-of-memory
data into memory and back is among major functions of a data source.
However you can also use a data source to implement an in-memory
non-numeric data transformation into a numeric form.
A numeric table is a key interface to operate on numeric in-memory
data. oneDAL supports several important cases of a numeric data
layout: homogeneous tables, arrays of structures, and structures of
arrays, as well as Compressed Sparse Row (CSR) encoding for sparse
oneDAL algorithms operate with in-memory numeric data accessed
through Numeric table interfaces.