Relational databases are great. Great for what they are built for – consistently managing complex data. This requires splitting an object, e.g. the “Patient” or the “Customer”, into atomic entities and storing different parts in different tables. Once distributed across many tables, however, an object is hard to analyze “as a whole” – and that is where today’s databases and analytical technologies fall short.
Xplain organizes data in a different, “object-centric” way and provides access to objects as a whole. You can define operations on those objects and iterate over them. To really reap from the benefits you can use the Object-Map-Reduce interface to execute an operation massively parallel on millions of stored object instances.
Algorithms previously painful to implement are now easy to apply. Novel algorithms – previously simply unimaginable – are becoming feasible.
Object Analytics will propel the field of Data Analytics and Artificial Intelligence into novel orbits.
Statisticians know very well that missing information leads to flawed conclusions on “causal” effects – and this also holds if you can analyze just parts of a complex object at a time. Xplain’s holistic Object Analytics Database therefore constitutes novel opportunities to uncover “causal” dependencies and separate them from myriads of meaningless correlations. Based on novel algorithm development capabilities, Xplain Data has succeeded to implement a class of predictive models which – in a complex data environment – builds any conceivable feature and amongst those identifies “causal” drivers for a target.
Knowing causal dependencies means being able to influence a system – a major step towards intelligent systems in real-world environments.
Let’s test-drive this on your data. Get in touch!
The “Object Explorer” is Xplain’s web-based frontend to view and analyze objects statistically.
This frontend allows you to analyze objects “as a whole” across all available data streams – instead of keyhole views which result from classical DWH approaches and replication of data into “Star schemas” or “OLAP cubes”. No need for experts to coerce data into those constraint analytical schemas, and with that you bring analytics from the ivory tower into your daily business: Interactively follow your train of thoughts from questions to follow-up questions and – supported by predictive models – discover potential “cause and effect” relationships.
Load data form source systems and store them in an object-centric format. Different interfaces then allow you to work with data stored as objects:
Our REST-based interface enables you to code in whatever language you prefer: R, R-Shiny, Java, PHP, C#, …! Use our generic Web API to connect your application to an Xplain Data backend and query your data.
Define an operation on an object and execute it massively parallel on millions of stored object instances. With this interface, you can inject algorithms deeply into the core engine of the database. Algorithms come to data instead the data to algorithms: no moving tons of data, no expensively transforming data into constraint formats till they can be sent to and processed by an algorithm.
We help early adopter customers which are challenged with complex analytics. We build applications for them aiming to propel them to the innovative forefront in their domain
We imagine algorithms that process any complex data as it is and – without the need to cast data into constraint schemas – live in a real world instead of an artificially prepared analytics environment.
... soon – with no need for human experts to pre-structure data – intelligent agents will autonomously be digging through complex and constantly changing data environments. They will detect likely “cause and effect” relationships, and – based on that knowledge – take actions to achieve desired outcome.
“Each new idea passes through three stages. First, people will ridicule it. Second, it is violently opposed. Finally, it will be considered self-evident.” - Schopenhauer (1788 - 1860)
Founded in 2015, Xplain Data GmbH is 100% privately-owned and self-funded by the team. As a tiny team we have set off to develop some groundbreaking innovations in the context of big data and predictive analytics. Our mission is to change the game on how data is turned into intelligence.
We are not yet for the broad masses, but are looking for “visionary” or early adopter customers who – in close cooperation with us – want to bring leading edge intelligence into their portfolio and be a co-innovation partner to further develop our technology.
We are in particular keen on testing new innovation models such as “Excubation Innovation”. If you want to know more about combining established companies with small innovative start-ups and early secure access to strategic assets, please feel free to contact us.
has a PhD in Theoretical Physics and Neuroinformatics and more than 20 years' experience in developing analytics technologies at major companies like Siemens, Accenture and SAP. Before founding Xplain Data he worked as Chief Architect at SAP with a focus on Big Data Analytics. Earlier in his career, he co-founded a startup where he was responsible for the entire product lifecycle of analytics innovations. Michael gathered broad and unique knowledge spanning from database and BI-technologies to Statistics, Mathematics and Machine Learning. From numerous projects he knows how to apply those technologies in a business context.
holds a diploma in computer science and got a PhD for a thesis on structure searching in protein databases. He worked as a substitute professor of theoretical computer science at the Chair for Efficient Algorithms. For several years, Hanjo taught at TU Munich on fundamentals of algorithms and data structures. He also gave master level lectures on computational biology and advanced network and graph algorithms. Hanjo is an expert in algorithms and data structures. In particular, he is a specialist for bioinformatics and graph/network-related problems.
holds a PhD in applied computer science for developing algorithms to deal with structural changes in Data Warehouses. He worked for more than 25 years in the Life Science and Health Care industry, working on projects for different hospitals, insurance companies and pharmaceutical companies. Christian has strong hands on experience with different Business Intelligence tools, database management systems and many programming languages and frameworks. During the last decade he focused on the development of different web applications.