Wednesday 9 February 2022

Can data modeling be enhanced by incorporating business knowledge?

If the answer is yes, then in what extent and how?

In this blog post we will try to answer the question by approaching a real-life scenario. We will discuss the challenges related to high amounts of data and the resulting processing times if the access to that data is not optimally planned and the structure of the data is not designed in a proper way. Finally, we will see how business knowledge about the scenario will provide information about the data modelling, which will boost the performance significantly.

Use Case

In a recent engagement, we worked with a company which decided to install Entry-Exit logging systems, as well as beacons at multiple locations inside its large plant area in order to monitor the entries and exits of the employees, as well as their movements within the plant. All employees were provided with a Bluetooth-equipped bracelet in order to be registered by beacons. Beacons are small, wireless transmitters that use low-energy Bluetooth technology to send signals to other smart devices nearby, bracelets on our scenario. The number of the beacons installed depends on the coverage that each company wants to achieve and the frequency of Beacon transmission is related to the level of detail required for the movement analysis. Details can be seen in the following figure

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In addition to simply tracking the employees, the data can be used in further Analytic and Data Science projects, such as

◉ Working behavior / efficiency of any employee.
◉ Total working hours.
◉ Overtime stay within the plant.
◉ Security constraints.
◉ Identify all employees which interacted with a Covid diagnosed colleague within the last N day(s).

As a next step, let us review the data model.

Data model in SAP HANA Cloud

For the engagement with the customer, the information described above was used to create a data model in SAP HANA Cloud. The level of detail depends always on our requirements and needs, but in our scenario we will concentrate on information regarding shift period and beacon appearance. Of course, many other entities can be created if needed, such as Beacon, Entry-Exit ones or information from external  API’s(such as weather, etc.). Moreover, employee information can be imported from any other internal system (e.g., census data like age, married status, years of work) and any organization structure.

In order to keep our scenario as simple as possible, let’s assume that all needed information is already populated to two main tables. The first table keeps information regarding the shift period per employee and the second one information regarding the beacon appearance of all employees. A simplified example of the structure of those tables is shown below.

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Since we want to use the combined information from both tables, a quick solution is a join between them on Employee level and a selection of all beacon appearances per employee between starting and ending of his/her shift, as shown below :

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In the context of as POC, when dealing with small amounts of information, this approach could be accepted. But if we translate this scenario into a productive one with realistic numbers, we will easily realize that this can go really wild. A real-life production scenario could involve the following

◉ Calculation for more than 10.000 employees. (I)
◉ Period of observation is at least three months. E.g. 70 working days per employee during this period. (II)
◉ Average working hours per shift is nine hours. (III)
◉ Average appearance of Employee on beacon is 60 times per hour (1 appearance per minute, in reality it can even down to seconds). (IV)

So, let’s do the math and try to calculate how many records we will require per employee to describe his movement data :

10.000 (I) * 70 (II) * 9 (III) * 60 (IV) = 378 million rows
378 million rows will need to be accessed for every employee, to assign his/her correct Beacon appearance per shift. Even if we still have not realized the performance issue, we will understand it after couple of hours when the query finally finishes. But, what if we bring Business and IT worlds together , taking advantage the SAP HANA partition feature, is it possible to enhance the Physical model and thus accelerate processing?

SAP HANA database provides the partition feature in order really big datasets from a table to be splitted on smallest ones based on one or more column(s).When a user will access the table, through SQL, providing appropriate values for the partitioned column(s), instead of a full table scan taken place, a search on the specific subsets will occur.

In our case, the key words that drive the scenario are “Employee” and “Shift Date“. How can we provide this information to our SAP HANA model in order to take advantage of them. What if we use those to partition our table and thus break down the million rows into smaller chunks for further processing. As we can see, “Employee” information is already there but “Shift Date” is missing on both tables. But, we have information regarding shift Starting / Ending as well as all Beacons appearances with corresponding timestamp. So, why not create a derived Date column on both tables from the timestamp ones, as shown in the figure below. To make SAP HANA aware about those two relationships, a two level of partitioning on both tables must take place, first level on Employee and second on the previous derived Date Column.

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So, let’s do the math again and calculate how many records we will require to search per employee to describe his movement now:

70 (II) * 9 (III) * 60 (IV) = 37.800 rows

This means that per employee we will need to access 37.800 rows for all his/her 70 shifts.

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