computer science

Hierarchical Data Format

Hierarchical Data Format is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Hierarchical Data Format rather than just read about it. In short: Hierarchical Data Format (HDF) is a set of file formats (HDF4, HDF5) designed to store and organize large amounts of data. Originally developed at the U.S.

Key takeaways

Hierarchical Data Format belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Hierarchical Data Format to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Hierarchical Data Format from memory before moving on to harder problems.

Reference excerpt

Hierarchical Data Format (HDF) is a set of file formats (HDF4, HDF5) designed to store and organize large amounts of data. Originally developed at the U.S. National Center for Supercomputing Applications, it is supported by The HDF Group, a non-profit corporation whose mission is to ensure continued development of HDF5 technologies and the continued accessibility of data stored in HDF. In keeping with this goal, the HDF libraries and associated tools are available under a liberal, BSD-like license for general use. HDF is supported by many commercial and non-commercial software platforms and programming languages. The freely available HDF distribution consists of the library, command-line utilities, test suite source, Java interface, and the Java-based HDF Viewer (HDFView). The current version, HDF5, differs significantly in design and API from the major legacy version HDF4.

Early history The quest for a portable scientific data format, originally dubbed AEHOO (All Encompassing Hierarchical Object Oriented format) began in 1987 by the Graphics Foundations Task Force (GFTF) at the National Center for Supercomputing Applications (NCSA). NSF grants received in 1990 and 1992 were important to the project. Around this time NASA investigated 15 different file formats for use in the Earth Observing System (EOS) project. After a two-year review process, HDF was selected as the standard data and information system.

HDF4 HDF4 is the older version of the format, although still actively supported by The HDF Group. It supports a proliferation of different data models, including multidimensional arrays, raster images, and tables. Each defines a specific aggregate data type and provides an API for reading, writing, and organizing the data and metadata. New data models can be added by the HDF developers or users. HDF is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can hold a mix of related objects which can be accessed as a group or as individual objects. Users can create their own grouping structures called "vgroups." The HDF4 format has many limitations. It lacks a clear object model, which makes continued support and improvement difficult. Supporting many different interface styles (images, tables, arrays) leads to a complex API. Support for metadata depends on which interface is in use; SD (Scientific Dataset) objects support arbitrary named attributes, while other types only support predefined metadata. Perhaps most importantly, the use of 32-bit signed integers for addressing limits HDF4 files to a maximum of 2 GB, which is unacceptable in many modern scientific applications.

HDF5 The HDF5 format is designed to address some of the limitations of the HDF4 library, and to address current and anticipated requirements of modern systems and applications. In 2002 it won an R&D 100 Award. HDF5 simplifies the file structure to include only two major types of object:

Datasets, which are typed multidimensional arrays Groups, which are container structures that can hold datasets and other groups This results in a truly hierarchical, filesystem-like data format. In fact, resources in an HDF5 file can be accessed using the POSIX-like syntax /path/to/resource. Metadata is stored in the form of user-defined, named attributes attached to groups and datasets. More complex storage APIs representing images and tables can then be built up using datasets, groups and attributes. In addition to these advances in the file format, HDF5 includes an improved type system, and dataspace objects which represent selections over dataset regions. The API is also object-oriented with respect to datasets, groups, attributes, types, dataspaces and property lists. The latest version of NetCDF, version 4, is based on HDF5. Because it uses B-trees to index table objects, HDF5 works well for time series data such as stock price series, network monitoring data, and 3D meteorological data. The bulk of the data goes into straightforward arrays (the table objects) that can be accessed much more quickly than the rows of an SQL database, but B-tree access is available for non-array data. The HDF5 data storage mechanism can be simpler and faster than an SQL star schema.

Feedback Criticism of HDF5 follows from its monolithic design and lengthy specification.

HDF5 does not enforce the use of UTF-8, so client applications may be expecting ASCII in most places. Dataset data cannot be freed in a file without generating a file copy using an external tool (h5repack).

Officially supported APIs C C++ CLI - .NET Fortran, Fortran 90 HDF5 Lite (H5LT) – a light-weight interface for C HDF5 Image (H5IM) – a C interface for images or rasters HDF5 Table (H5TB) – a C interface for tables HDF5 Packet Table (H5PT) – interfaces for C and C++ to handle "packet" data, accessed at high-speeds HDF5 Dimension Scale (H5DS) – allows dimension scales to be added to HDF5 Java

See also Common Data Format (CDF) FITS, a data format used in astronomy GRIB (GRIdded Binary), a data format used in meteorology HDF Explorer NetCDF, The Netcdf Java library reads HDF5, HDF4, HDF-EOS and other formats using pure Java Protocol Buffers - Google's data interchange format Zarr, a data format with similarities to HDF5

References

External links Official website What is HDF5? HDF-EOS Tools and Information Center Open Navigation Surface

Illustrations

Worked examples

Example 1 — a first encounter with Hierarchical Data Format

Start with the simplest possible case. Write down what Hierarchical Data Format claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Hierarchical Data Format before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Hierarchical Data Format ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Hierarchical Data Format

In research: Hierarchical Data Format appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Hierarchical Data Format in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Hierarchical Data Format is common in secondary-school and first-year university syllabi. It links to neighbouring topics C (programming language) libraries, Computer file formats, Earth sciences data formats, so understanding it makes those chapters shorter.
In everyday life: Look for Hierarchical Data Format outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Hierarchical Data Format in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Hierarchical Data Format means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Hierarchical Data Format out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Hierarchical Data Format in simple terms?

Hierarchical Data Format (HDF) is a set of file formats (HDF4, HDF5) designed to store and organize large amounts of data. Originally developed at the U.S.

Why does Hierarchical Data Format matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Hierarchical Data Format?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Hierarchical Data Format.