LEGUME DATABASE
CONTAINER SYSTEM
New database
Methodology
Redwing Technologies introduces you to a new database methodology
that was recently invented by Datura, LLC (USA). Like with any new
technology the Legume Database Container needs fundamental understanding
before assessment can be made of its applicability and capability.
·
Merge or integrate some or all your
databases
·
Store variable or object data in a relational database
·
Uniform data
containers for easier data
distribution
·
Store legacy data in an accessible manner
·
Lower costs of development
and maintenance
·
High flexibility of
data and development.
Equal Format Databases
Legume is a database container system that can
hold all of your data in a single
relational database design – the equal format database. Its
unique table structure is scalable, reusable
and can be built on any database
server from mainframe to portable devices. Its tables can be added into
existing databases to give them added capabilities. You can build or
enhance applications more easily with reusable,
reliable components. The uniform
structure of Equal Format Databases makes it easier to store, move and
manipulate complex data. This new database technology is patent-pending
and, with its patents being described by a World Trade Organisation Group
as “Novel and Inventive”, is fully operational now.
Time And Cost Saving
Major cost benefits are
achieved by using equal format database containers:
·
Cross-platform
system design allows use of
almost any database system vendor
·
Standard relational
design can be used in current systems thus preserving past training and tools
·
Repeat use of a common structure allows
building of reusable, reliable
and secure software
·
Ready-to-go
common design allows faster application development with less gridlock and
easier data distribution
·
Merges databases
and updates legacy data.
Unique Benefits
·
Store complex,
variable data efficiently in a powerful relational database and in a
straightforward manner
·
More flexibility
than XML and RDF and just as portable
·
Incremental,
non-destructive storage provides regulatory data tracking and efficient
data snapshots from any point-in-time
·
Search across
columns as easy as rows
·
Store multiple
values and types in a field.
Datura, LLC
Datura was founded in
2005 as a software laboratory to develop advanced database technology based
on years of experience in large-scale commercial databases, technical
databases and off-the-shelf application development. Datura invented the
concept of Equal Format Databases and has filed a patent on a robust,
commercial version of the technology. Refer to www.datura-llc.com for
more information.
The Future of Databases
You are invited to try
a single-user version of the technology by downloading BeanCounter, a
desktop application built with the Legume database container system. To try
the software or read more details, go to the special website at http://www.datura-llc.com/beancounterbetadl.html.
Any objective feedback will be most welcome and if necessary responded to.
Legume is a relational
database container built on standard server systems, but differs in
that it is already designed, normalized, and ready to use for now
and for future changes. It is robust enough to store any kind or mixture of
logical data structure - relational, hierarchical, object, or semantic.
Because it’s a relational structure it can be integrated into
existing relational systems and
because it is uniform data management becomes standardized,
optimized, and reusable, demonstrated by our standard client program,
BeanCounter, which can be used as a platform for other client applications.
Datura’s
differentiation is that Legume is an “Equal Format Database”,
which is a relational database design where the table structure is
independent of the data content and logical structure, making it
super-normalized in traditional terms. Datura’s implementation of
Equal Format Databases uses a structure and methodology that allows for
enterprise-wide and global data management with high-volume transaction and
distribution capabilities.
The Legume Database Container Overview
·
Because of its
reusability, projects take less time and cost less
·
Because it can
hold any kind of data, it can handle future needs
·
Because of its
uniform design, data is more easily shared
·
Its design
allows easy data merging despite differences among data
·
Its modular
system is scalable – from the smallest project to supercomputing
·
Can give the
point-in-time data snapshots that engineers and researchers need
·
Excellent for
regulation compliance and security data tracking
·
Can revive
legacy databases’ usefulness.
Examples - Legume Technology Markets
·
Corporate
Database Systems and Enterprise
Architecture: Small
companies have dozens of databases and large companies have thousands of
them. Each database will average a hundred or more tables, each averaging a
dozen columns. Any business changes, legal changes, mergers, system
enhancements or departmental collaborations lead to database changes. These
changes bring the additional burden of adapting the old (legacy) data and
software to the new system. Legume System allows system changes easily with
its flexible design, saving time and costs.
·
Databases
in Engineering and Design: Many of the same problems in corporate systems are
shared in the engineering and design world. It requires data in different
formats and analytical models to design and build or operate something.
Legume systems use an Equal Format to allow these formats and models to
work together. Gives engineers
point in time data retrieval on equipment, power systems, tracking systems
etc.
·
Medical
/ Retail Records Databases: As medical / retail
records are computerized to save costs, different formats and models will
be merging and working together.
Compiling, storing and accessing complex data will require
flexibility and reliability. Legume technology can handle it, with its
unique data container design. All types of data can be easily integrated
and equally accessed.
Examples - Market Situations
·
Legacy Databases: We lower the cost of importing old
databases into a new relational database system, and integrating it with
the new system, regardless of the original data structure. This is a big
cost savings in a project.
·
Mergers: Because you do not have to design a new composite database,
we lower the cost of merging databases, especially from different
organizational structures. Some mergers can be extremely difficult to do
otherwise, especially if one of them is not relational already.
·
Data Tracking Repository with Point-in-Time
Retrieval: The
atomic nature of the data storage and its time-tracking organization
provide an incremental mode of storage that tracks all changes (by user and
time) in a way that you can retrieve a frozen snapshot of the database from
any point in time of its history as easily as you can retrieve the current
state.
·
Regulatory Repository: The flexible nature of the structure easily
allows storing any record from any database, and views it with our client
viewer, BeanCounter. Accounting and management can peer into the process at
all stages.
·
Transactional Data Distribution and
Messaging: The
flexible, atomic format of our databases allows general data distribution
systems to be created that can adapt to changes without breaking the
system. This allows publishers to add new data fields without having to
synchronize with the receivers of the data, avoiding the gridlock caused by
traditional designs.
·
Integration System: The same flexible storage capability needed
for mergers is also needed for merging department data. The standard
viewer, BeanCounter, makes it easy to view the data used by others without
extra programming. Delinking data from structural design relieves the
synchronization that causes gridlock.
·
Application Integration Platform: The same flexible storage capability needed
for mergers and data integration allows suites of applications to be built
that work together more easily without the gridlock caused by integration of
traditional designs.
·
Analysis Data and Unusual Logical
Structures:
Data comes in different structures – mathematical, hierarchical,
relational, physical, object, semantic, and mixtures. We avoid much of the difficulty
of designing new structures for new each batch of data collected in
research and operations.
·
Data Warehousing, Data Marts, and Data
Mining: The atomic,
relational nature of our design makes easier connections between data from
all sources and transformations. New tables do not have to be designed for
each question, and business intelligence can become smarter with
standardized storage structures.
·
Master Data and Catalog Data: People, places, and things come with great
variance in data and interrelationships, and are constantly changing.
Searching and manipulating the data is restricted by the work needed to
deal with the constant changes added over time. The flexible structure
removes much of this burden and allows a path to growth and variance over
time.
·
Global Data Management: Our system design offers the unique
capabilities of taking our flexible containers, key system, and
methodologies to give you the control necessary to provide global data
distribution and management.
Examples
– Target Market
·
Enterprise Architects
·
Data
Architects
·
Chief
Information Officers
·
Architects
and Engineers
·
Researchers
·
Software
Companies.
