The Future of Digital - what the next 10 years has in store?

A complex time series in real life usually has many change points inside it. When dealing with such data, simply applying traditional seasonality test to it may not render a convincing decomposition result. In this blog post, we will show how to use Bayesian Change Point Detection in the Python machine learning client for SAP HANA(hana-ml) to detect those change points and decompose the target time series. Time series may not ideally contain monotonic trend and seasonal waves after decomposition. On the contrary, it may include a great many inner change points in those parts.

Modern technology has unlocked the data fabric of analytics with the potential of machine intelligence in day-to-day life. The field of Computer Science Engineering has contributed significantly to the development of various mathematical models and algorithms since the inception of earlier Konrad Zuse programmable computers. DeepSingularity LCC, is a global leading company in providing consultancy services for SAP, Big Data Analytics, data science, machine learning, deep learning, and IoT solutions. In the recent times, Enterprise Data Warehouse and SAP NetWeaver Business Warehouse have become intertwined for executive decision support systems based on running many data science and IoT platforms. Apparently, SAP holds the Guinness Book of World Records for building the largest big data warehouse with 12.1 PB big data running on SAP HANA (High-performance analytics appliance).The SAP solutions provider company handles projects integrating SAP HANA/SAP S/4 HANA with petabyte-scale data warehouses such as AWS RedShift and Google's BigQuery data science platforms requiring extensive data ingestion, data processing, data analytics, and programming.

UPDATE Oct, 2019: We just added a new season with 4 new podcasts focused on artificial intelligence, machine learning, data science, and data orchestration. Building a data foundation is essential to driving innovation. This is just as true for mid-market companies as for large enterprise companies. Mid-market and large enterprise companies have different challenges, so we've brought together experts from each size company to discuss key trends that are reshaping the way successful companies use their data: from data management and data foundation to spatial and machine learning to data-based process and information excellence. Listen to this chat series on all things data!

UPDATE Oct, 2019: We just added a new season with 4 new podcasts focused on artificial intelligence, machine learning, data science, and data orchestration. Building a data foundation is essential to driving innovation. This is just as true for mid-market companies as for large enterprise companies. Mid-market and large enterprise companies have different challenges, so we've brought together experts from each size company to discuss key trends that are reshaping the way successful companies use their data: from data management and data foundation to spatial and machine learning to data-based process and information excellence. Listen to this chat series on all things data!

Last time, we looked at how to use Jupyter notebooks to run kernels in Python and R and even SQL. I'll be honest, Jupyter is now my new "go to" tool. I even contributed to the SQLAlchemy for SAP HANA GitHub repository recently to add the support of HDB User Store, which will allow you to connect without providing your user credentials in one of the cells. I hope you all managed to try this out, and probably some of you already decided to switch from the "good old" Eclipse IDE and its SAP HANA Tools plugin to run your SQL. I know that I promised to dive into the TensorFlow integration over and over.

As part of the first SAP Esri Spatial Hackathon, GIS developers, enterprise architects, data scientists, BI developers, and students got together to solve a variety of challenges through the use of geospatial analytics and machine learning technology. With the help of NextGen and the SAP Co-Innovation Lab – who ran the technology behind the hackathon – teams were given 40 hours to define the issue at hand and develop compelling platforms that provide an effective solution that can be applied in real-world scenarios while 89% of participants had no prior experience with SAP HANA Spatial. Finalist team, We're Working on It, was a corporate team from Southern California Edison (SCE), the primary electricity supply company for much of Southern California. This team developed a solution to predict grid usage for solar, using SAP HANA, ArcGIS Pro, and R-ArcGIS Bridge to show which parts of the grid may need modernization to maintain reliability and support clean energy. The picture below shows the SAP HANA as the enterprise geodatabase for ArcGIS Pro.

With a new era of cloud and big data, Database Management Systems (DBMSs) have become more crucial in numerous enterprise business applications in all the industries. Accordingly, the importance of their proactive and preventive maintenance has also increased. However, detecting problems by predefined rules or stochastic modeling has limitations, particularly when analyzing the data on high-dimensional Key Performance Indicators (KPIs) from a DBMS. In recent years, Deep Learning (DL) has opened new opportunities for this complex analysis. In this paper, we present two complementary DL approaches to detect anomalies in SAP HANA. A temporal learning approach is used to detect abnormal patterns based on unlabeled historical data, whereas a spatial learning approach is used to classify known anomalies based on labeled data. We implement a system in SAP HANA integrated with Google TensorFlow. The experimental results with real-world data confirm the effectiveness of the system and models.

Machine learning and the world of artificial intelligence (AI) are no longer science fiction. Get started with the new breed of software that is able to learn without being explicitly programmed, machine learning can access, analyze, and find patterns in Big Data in a way that is beyond human capabilities. The business advantages are huge, and the market is expected to be worth $47 billion and more by 2020. In this course, you will implement your own custom algorithm on top of SAP's HANA Database, which is an In-Memory database capable of Performing huge calculation over a large set of Data. We are going to use Native SQL to write the algorithm of Naive Bayes.

In this blog I aim to provide an introduction to TensorFlow and the SAP HANA integration, give you an understanding of the landscape and outline the process for using External Machine Learning with HANA. There's plenty of hype around Machine Learning, Deep Learning and of course Artificial Intelligence (AI), but understanding the benefits in an enterprise context can be more challenging. Being able to integrate the latest and greatest deep learning models into your enterprise via a high performance in-memory platform could provide a competitive advantage or perhaps just keep up with the competition? With HANA 2.0 SP2 onwards we have the ability to call TensorFlow (TF) models or graphs as they are known. HANA now includes a method to call External Machine Learning (EML) models via a remote source.