This is a relational database schema for a sales and order management system, designed to track customers, employees, products, orders, and payments. Below is a detailed breakdown of each table and their relationships:

1. productlines Table (Product Categories)

• Represents different product categories.
• Primary Key: productLine
• Attributes:
  • textDescription: A short description of the product line.
  • htmlDescription: A detailed HTML-based description.
  • image: Associated image (if applicable).
• Relationships:
  • One-to-Many with products: Each product belongs to one productLine.

2. products Table (Product Information)

• Stores details of individual products.
• Primary Key: productCode
• Attributes:
  • productName: Name of the product.
  • productLine: Foreign key linking to productlines.
  • productScale, productVendor, productDescription: Additional product details.
  • quantityInStock: Number of units available.
  • buyPrice: Cost price per unit.
  • MSRP: Manufacturer's Suggested Retail Price.
• Relationships:
  • Many-to-One with productlines (each product belongs to one category).
  • One-to-Many with orderdetails (a product can be part of many orders).

3. orderdetails Table (Line Items in an Order)

• Stores details of each product within an order.
• Composite Primary Key: (orderNumber, productCode)
• Attributes:
  • quantityOrdered: Number of units in the order.
  • priceEach: Price per unit.
  • orderLineNumber: The sequence number in the order.
• Relationships:
  • Many-to-One with orders (each order has multiple products).
  • Many-to-One with products (each product can appear in multiple orders).

4. orders Table (Customer Orders)

• Represents customer orders.
• Primary Key: orderNumber
• Attributes:
  • orderDate: Date when the order was placed.
  • requiredDate: Expected delivery date.
  • shippedDate: Actual shipping date (can be NULL if not shipped).
  • status: Order status (e.g., "Shipped", "In Process", "Cancelled").
  • comments: Additional remarks about the order.
  • customerNumber: Foreign key linking to customers.
• Relationships:
  • One-to-Many with orderdetails (an order contains multiple products).
  • Many-to-One with customers (each order is placed by one customer).

5. customers Table (Customer Details)

• Stores customer information.
• Primary Key: customerNumber
• Attributes:
  • customerName: Name of the customer.
  • contactLastName, contactFirstName: Contact person.
  • phone: Contact number.
  • addressLine1, addressLine2, city, state, postalCode, country: Address details.
  • salesRepEmployeeNumber: Foreign key linking to employees, representing the sales representative.
  • creditLimit: Maximum credit limit assigned to the customer.
• Relationships:
  • One-to-Many with orders (a customer can place multiple orders).
  • One-to-Many with payments (a customer can make multiple payments).
  • Many-to-One with employees (each customer has a sales representative).

6. payments Table (Customer Payments)

• Stores payment transactions.
• Composite Primary Key: (customerNumber, checkNumber)
• Attributes:
  • paymentDate: Date of payment.
  • amount: Payment amount.
• Relationships:
  • Many-to-One with customers (each payment is linked to a customer).

7. employees Table (Employee Information)

• Stores details of employees, including reporting hierarchy.
• Primary Key: employeeNumber
• Attributes:
  • lastName, firstName: Employee's name.
  • extension, email: Contact details.
  • officeCode: Foreign key linking to offices, representing the employee's office.
  • reportsTo: References another employeeNumber, establishing a hierarchy.
  • jobTitle: Employee’s role (e.g., "Sales Rep", "Manager").
• Relationships:
  • Many-to-One with offices (each employee works in one office).
  • One-to-Many with employees (self-referential, representing reporting structure).
  • One-to-Many with customers (each employee manages multiple customers).

8. offices Table (Office Locations)

• Represents company office locations.
• Primary Key: officeCode
• Attributes:
  • city, state, country: Location details.
  • phone: Office contact number.
  • addressLine1, addressLine2, postalCode, territory: Address details.
• Relationships:
  • One-to-Many with employees (each office has multiple employees).

Conclusion

This schema provides a well-structured design for managing a sales and order system, covering: ✅ Product inventory
✅ Order and payment tracking
✅ Customer and employee management
✅ Office locations and hierarchical reporting

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Dataset Description

The E-commerce Order Dataset provides comprehensive information related to orders, items within orders, customers, payments, and products for an e-commerce platform. This dataset is structured with multiple tables, each containing specific information about various aspects of the e-commerce operations.

Dataset Features

Orders Table:

• order_id: Unique identifier for an order, acting as the primary key.
• customer_id: Unique identifier for a customer. This table may not be unique at this level.
• order_status: Indicates the status of an order (e.g., delivered, cancelled, processing, etc.).
• order_purchase_timestamp: Timestamp when the order was made by the customer.
• order_approved_at: Timestamp when the order was approved from the seller's side.
• order_delivered_timestamp: Timestamp when the order was delivered at the customer's location.
• order_estimated_delivery_date: Estimated date of delivery shared with the customer while placing the order.

Order Items Table

• order_id: Unique identifier for an order.
• order_item_id: Item number in each order, acting as part of the primary key along with the order_id.
• product_id: Unique identifier for a product.
• seller_id: Unique identifier for the seller.
• price: Selling price of the product.
• shipping_charges: Charges associated with the shipping of the product.

Customers Table

• customer_id: Unique identifier for a customer, acting as the primary key.
• customer_zip_code_prefix: Customer's Zip code.
• customer_city: Customer's city.
• customer_state: Customer's state.

Payments Table

• order_id: Unique identifier for an order.
• payment_sequential: Provides information about the sequence of payments for the given order.
• payment_type: Type of payment (e.g., credit_card, debit_card, etc.).
• payment_installments: Payment installment number in case of credit cards.
• payment_value: Transaction value.

Products Table

• product_id: Unique identifier for each product, acting as the primary key.
• product_category_name: Name of the category the product belongs to.
• product_weight_g: Product weight in grams.
• product_length_cm: Product length in centimeters.
• product_height_cm: Product height in centimeters.
• product_width_cm: Product width in centimeters.

Dataset Overview

Dataset Name: "Nuestro Amazon" E-Commerce Dataset

General Description: This dataset represents an e-commerce database containing information about products, categories, customers, orders, and more. The data is structured to facilitate analysis and insights into various aspects of an e-commerce business.

Structure and Attributes: The dataset consists of eight tables: categories, customers, employees, orders, ordersdetails, products, shippers, and suppliers. These tables encompass key information such as product details, customer information, order details.

Data Source: The data was generated for educational and demonstration purposes to simulate an e-commerce environment. It is not sourced from a real-world e-commerce platform.

Usage and Applications: This dataset can be utilized for various purposes, including market basket analysis, customer segmentation, sales trends analysis, and supply chain optimization. Analysts and data scientists can derive valuable insights to improve business strategies.

Acknowledgments and References: The dataset was created for educational use. No specific external sources were referenced for this dataset.

Explore Interactive Visualizations

"Quantity per country" in this Kaggle notebook or on Tableau.

"Orders by country" in this Kaggle notebook or on Tableau.

Data Analysis

"Data Analysis of Online Orders" in this Kaggle notebook

"Data Visualization and Analysis in R" in this Kaggle notebook

The statistical data generated through the administration of the Federal milk order program is recognized widely as one of the benefits of this program. These data provide comprehensive and accurate information on milk supplies, utilization, and sales, as well as class prices established under the orders and prices paid to dairy farmers (producers). The sources of this data are monthly reports of receipts and utilization, producer payroll reports, and reports of nonpool handlers filed by milk processors (handlers) subject to the provisions of the various milk orders. The local market administrator (MA) uses these reports to determine pool obligations under the order and to verify proper payments to producers. Auditors employed by the MA review handler records to assure the accuracy of reported information. Reporting errors are corrected; if necessary, pool obligations are revised. After the pool obligations have been determined the local market administrator summarizes the individual handler reports and submits a series of order summary reports to the Market Information Branch (MIB) in Dairy Programs. The MIB summarizes the individual order data and disseminates this information via monthly, bimonthly, and annual releases or publications. Since milk marketing order statistics are based on reports filed by the population of possible reporting firms and not a sample, these statistics are comprehensive. Also, since these individual firm reports are subject to audit and verification, these statistics are accurate. The Federal milk order statistics database contains historical information, beginning in January 2000, generated by the administration of the Federal milk order program. Most of the information in the database has been published previously by the Market Information Branch in Dairy Programs either on its web site or in the Dairy Market News Report. New users are encouraged to use the "User Guide" to learn how to navigate the search screens. If you are interested in a description of the Federal milk order statistics program, or want current data, in ready made table form, use the "Current Information" link.

This dataset has details of orders placed by customers to the restaurants in a food delivery app. There are 500 orders that were placed on a day.

Join both the tables in this dataset to get the complete data.

You can use dataset to find the patterns in the orders placed by customers. You can analyze this dataset to find the answers to the below questions. 1) Which restaurant received the most orders? 2) Which restaurant saw most sales? 3) Which customer ordered the most? 4) When do customers order more in a day? 5) Which is the most liked cuisine? 6) Which zone has the most sales?

Please upvote if you like my work.

Disclaimer: The names of the customers and restaurants used are only for representational purposes. They do not represent any real life nouns, but are only fictional.

Abstract: This repository/dataset provides a suite of Python scripts to generate a simulated relational database for inventory management processes and transform this data into object-centric event logs (OCEL) suitable for advanced process mining analysis. The primary goal is to offer a synthetic yet realistic dataset that facilitates research, development, and application of object-centric process mining techniques in the domain of inventory control and supply chain management. The generated event logs capture common inventory operations, track stock level changes, and are enriched with key inventory management parameters (like EOQ, Safety Stock, Reorder Point) and status-based activity labels (e.g., indicating understock or overstock situations).

Overview: Inventory management is a critical business process characterized by the interaction of various entities such as materials, purchase orders, sales orders, plants, suppliers, and customers. Traditional process mining often struggles to capture these complex interactions. Object-Centric Process Mining (OCPM) offers a more suitable paradigm. This project provides the tools to create and explore such data.

The workflow involves:

1. Database Simulation: Generating a SQLite database with tables for materials, sales orders, purchase orders, goods movements, stock levels, etc., populated with simulated data.
2. Initial OCEL Generation: Extracting data from the SQLite database and structuring it as an object-centric event log (in CSV format). This log includes activities like "Create Purchase Order Item", "Goods Receipt", "Create Sales Order Item", "Goods Issue", and tracks running stock levels for materials.
3. OCEL Post-processing and Enrichment:
   • Calculating standard inventory management metrics such as Economic Order Quantity (EOQ), Safety Stock (SS), and Reorder Point (ROP) for each material-plant combination based on the simulated historical data.
   • Merging these metrics into the event log.
   • Enhancing activity labels to include the current stock status (e.g., "Understock", "Overstock", "Normal") relative to calculated SS and Overstock (OS) levels (where OS = SS + EOQ).
   • Generating new, distinct events to explicitly mark the moments when stock statuses change (e.g., "START UNDERSTOCK", "ST CHANGE NORMAL to OVERSTOCK", "END NORMAL").
4. Format Conversion: Converting the CSV-based OCELs into the standard OCEL XML/OCEL2 format using the pm4py library.

Contents:

The repository contains the following Python scripts:

• 01_generate_simulation.py:

  • Creates a SQLite database named inventory_management.db.
  • Defines and populates tables including: Materials, SalesOrderDocuments, SalesOrderItems, PurchaseOrderDocuments, PurchaseOrderItems, PurchaseRequisitions, GoodsReceiptsAndIssues, MaterialStocks, MaterialDocuments, SalesDocumentFlows, and OrderSuggestions.
  • Simulates data for a configurable number of materials, customers, sales, purchases, etc., with randomized dates and quantities.

• 02_database_to_ocel_csv.py:

  • Connects to the inventory_management.db.
  • Executes a SQL query to extract relevant events and their associated objects for inventory processes.
  • Constructs an initial object-centric event log, saved as ocel_inventory_management.csv.
  • Identified object types include: MAT (Material), PLA (Plant), PO_ITEM (Purchase Order Item), SO_ITEM (Sales Order Item), CUSTOMER, SUPPLIER.
  • Calculates "Stock Before" and "Stock After" for each event affecting material stock.
  • Standardizes column names to OCEL conventions (e.g., ocel:activity, ocel:timestamp, ocel:type:).

• 03_ocel_csv_to_ocel.py:

  • Reads ocel_inventory_management.csv.
  • Uses pm4py to convert the CSV event log into the standard OCEL XML format (ocel_inventory_management.xml).

• 04_postprocess_activities.py:

  • Reads data from inventory_management.db to calculate inventory parameters:
    • Annual Demand (Dm)
    • Average Daily Demand (dm)
    • Standard Deviation of Daily Demand (σm)
    • Average Lead Time (lm)
    • Economic Order Quantity (EOQ): (2⋅Dm⋅S)/H (where S is fixed order cost, H is holding cost)
    • Safety Stock (SS): z⋅σm⋅lm (where z is the z-score for the desired service level)
    • Reorder Point (ROP): (dm⋅lm)+SS
  • Merges these calculated parameters with ocel_inventory_management.csv.
  • Computes an Overstock level (OS) as SS+EOQ.
  • Derives a "Current Status" (Understock, Overstock, Normal) for each event based on "Stock After" relative to SS and OS.
  • Appends this status to the ocel:activity label (e.g., "Goods Issue (Understock)").
  • Generates new events for status changes (e.g., "START NORMAL", "ST CHANGE UNDERSTOCK to NORMAL", "END OVERSTOCK") with adjusted timestamps to precisely mark these transitions.
  • Creates a new object type MAT_PLA (Material-Plant combination) for easier status tracking.
  • Saves the enriched and transformed log as post_ocel_inventory_management.csv.

• 05_ocel_csv_to_ocel.py:

  • Reads the post-processed post_ocel_inventory_management.csv.
  • Uses pm4py to convert this enriched CSV event log into the standard OCEL XML format (post_ocel_inventory_management.xml).

Generated Dataset Files (if included, or can be generated using the scripts):

• inventory_management.db: The SQLite database containing the simulated raw data.
• ocel_inventory_management.csv: The initial OCEL in CSV format.
• ocel_inventory_management.xml: The initial OCEL in standard OCEL XML format.
• post_ocel_inventory_management.csv: The post-processed and enriched OCEL in CSV format.
• post_ocel_inventory_management.xml: The post-processed and enriched OCEL in standard OCEL XML format.

How to Use:

1. Ensure you have Python installed along with the following libraries: sqlite3 (standard library), pandas, numpy, pm4py.
2. Run the scripts sequentially in a terminal or command prompt:
   • python 01_generate_simulation.py (generates inventory_management.db)
   • python 02_database_to_ocel_csv.py (generates ocel_inventory_management.csv from the database)
   • python 03_ocel_csv_to_ocel.py (generates ocel_inventory_management.xml)
   • python 04_postprocess_activities.py (generates post_ocel_inventory_management.csv using the database and the initial CSV OCEL)
   • python 05_ocel_csv_to_ocel.py (generates post_ocel_inventory_management.xml)

Potential Applications and Research: This dataset and the accompanying scripts can be used for:

• Applying and evaluating object-centric process mining algorithms on inventory management data.
• Analyzing inventory dynamics, such as the causes and effects of understocking or overstocking.
• Discovering and conformance checking process models that involve multiple interacting objects (materials, orders, plants).
• Investigating the impact of different inventory control parameters (EOQ, SS, ROP) on process execution.
• Developing educational materials for teaching OCPM in a supply chain context.
• Serving as a benchmark for new OCEL-based analysis techniques.

Keywords: Object-Centric Event Log, OCEL, Process Mining, Inventory Management, Supply Chain, Simulation, Synthetic Data, SQLite, Python, pandas, pm4py, Economic Order Quantity (EOQ), Safety Stock (SS), Reorder Point (ROP), Stock Status Analysis.

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Japan Information Service Sales: SDP: Orders data was reported at 857,742.000 JPY mn in Sep 2018. This records an increase from the previous number of 440,240.000 JPY mn for Aug 2018. Japan Information Service Sales: SDP: Orders data is updated monthly, averaging 428,739.000 JPY mn from Feb 2007 (Median) to Sep 2018, with 140 observations. The data reached an all-time high of 1,467,866.000 JPY mn in Mar 2008 and a record low of 294,947.000 JPY mn in Apr 2007. Japan Information Service Sales: SDP: Orders data remains active status in CEIC and is reported by Ministry of Economy, Trade and Industry. The data is categorized under Global Database’s Japan – Table JP.H016: Information Services Sales.

Japan Information Service Sales: SDP: Order: System Integration data was reported at 568,829.000 JPY mn in Sep 2018. This records an increase from the previous number of 299,427.000 JPY mn for Aug 2018. Japan Information Service Sales: SDP: Order: System Integration data is updated monthly, averaging 282,987.500 JPY mn from Feb 2007 (Median) to Sep 2018, with 140 observations. The data reached an all-time high of 911,537.000 JPY mn in Mar 2008 and a record low of 181,784.000 JPY mn in Apr 2007. Japan Information Service Sales: SDP: Order: System Integration data remains active status in CEIC and is reported by Ministry of Economy, Trade and Industry. The data is categorized under Global Database’s Japan – Table JP.H016: Information Services Sales.

In order to compare the performance of various Database Management Systems (DBMS), five primary tables—customer, salesman, category, qty_product, and product—were used to create an extensive test dataset. This data was then stored in two principal tables, transaction_hdr and transaction_dtl, each containing over 100,000 records. The utilization of this large dataset allows for a thorough evaluation of DBMS performance using tools such as JMeter.

Tables Used: 1. Customer: Stores information about customers, including customer ID, name, contact details, and address. 2. Salesman: Contains data about sales personnel, including salesman ID, name, contact details, and address. 3. Category_Product: Classifies products into specific categories, including category ID and category type. 4.Qty_Product: Maintains information regarding the quantity of products available, including quantity ID, quantity name, and quantity value. 5. Product: Details information about products, including product ID, product name, category ID, size, quantity ID, stock, and price.

By utilizing the above tables, a substantial dataset was generated by populating the transaction_hdr and transaction_dtl tables with over 100,000 records each. The transaction_hdr table includes transaction headers with information such as transaction ID, date, customer ID, salesman ID, and total price. The transaction_dtl table records the details of each transaction, including transaction ID, product ID, product name, category ID, quantity ID, quantity value, quantity, and product price. JMeter was employed to conduct performance testing on the DBMS using this dataset to assess throughput and response time across MySQL, Oracle, and PostgreSQL databases.

Dataset Card for "sales-conversations"

This dataset was created for the purpose of training a sales agent chatbot that can convince people. The initial idea came from: textbooks is all you need https://arxiv.org/abs/2306.11644 gpt-3.5-turbo was used for the generation

  Structure

The conversations have a customer and a salesman which appear always in changing order. customer, salesman, customer, salesman, etc. The customer always starts the conversation Who ends the… See the full description on the dataset page: https://huggingface.co/datasets/goendalf666/sales-conversations.

Context We are a bank that has a database with a large amount of information about our customers. Our goal is to help analysts predict the churn rate of these customers in order to reduce it. The database includes demographic information such as age, gender, marital status and income category. It also contains information on card type, number of months in portfolio and inactive periods. In addition, it has key data on the spending behavior of customers approaching their cancellation decision. Among the latter information are the total renewable balance, the credit limit, the average open-to-buy rate and analyzable metrics such as the total amount of change from the fourth quarter to the first quarter or the average utilization rate.

Against this data set we can capture up-to-date information that can determine the long-term stability of the account or its imminent exit.

Objective Help analysts predict the churn rate of these customers in order to reduce it.

Create a predictive classification model in order to classify the data in the test file.

The technological development in the new economic era has brought challenges to enterprises. Enterprises need to use massive and effective consumption information to provide customers with high-quality customized services. Big data technology has strong mining ability. The relevant theories of computer data mining technology are summarized to optimize the marketing strategy of enterprises. The application of data mining in precision marketing services is analyzed. Extreme Gradient Boosting (XGBoost) has shown strong advantages in machine learning algorithms. In order to help enterprises to analyze customer data quickly and accurately, the characteristics of XGBoost feedback are used to reverse the main factors that can affect customer activation cards, and effective analysis is carried out for these factors. The data obtained from the analysis points out the direction of effective marketing for potential customers to be activated. Finally, the performance of XGBoost is compared with the other three methods. The characteristics that affect the top 7 prediction results are tested for differences. The results show that: (1) the accuracy and recall rate of the proposed model are higher than other algorithms, and the performance is the best. (2) The significance p values of the features included in the test are all less than 0.001. The data shows that there is a very significant difference between the proposed features and the results of activation or not. The contributions of this paper are mainly reflected in two aspects. 1. Four precision marketing strategies based on big data mining are designed to provide scientific support for enterprise decision-making. 2. The improvement of the connection rate and stickiness between enterprises and customers has played a huge driving role in overall customer marketing.

Korea Machinery Orders: Sales Amount data was reported at 6,190.649 KRW bn in Sep 2018. This records an increase from the previous number of 6,028.996 KRW bn for Aug 2018. Korea Machinery Orders: Sales Amount data is updated monthly, averaging 5,704.538 KRW bn from Jun 1996 (Median) to Sep 2018, with 268 observations. The data reached an all-time high of 10,041.489 KRW bn in Mar 2009 and a record low of 1,995.547 KRW bn in Aug 1996. Korea Machinery Orders: Sales Amount data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s South Korea – Table KR.C032: Value of Machinery Orders Received.

This metric is the percentage of time that SSC completes a hardware request ordered against SSC Virtual Inventory offers. Calculation / formula: Number of hardware requests ordered against SSC Virtual Inventory completed within 10 days / total number of hardware requests ordered against SSC Virtual Inventory completed X 100 = % Baseline: 10 days for processing Hardware requests ordered against SSC Virtual Inventory offers 90.00% of the time. Definitions: Defined in accordance with the Treasury Board Contracting Policy and Notice CPN 2007-4. As a result of executing Requests for Volume Discounts (RVD), SSC maintains virtual inventories of devices enabling customers to buy devices at discounted prices and with very fast turnaround times. Note(s): Customer chooses a WTD hardware item from the SSC ITPRO estore, the user then “checks out” their order and creates an order number. Once the order number is created, the processing time for WTD begins. This metric starts to get tracked once the customer creates the order number with WTD Provisioning. WTD then keeps track of all its HW orders in the ITPRO estore database. Target: 90.00% (SSC completes a hardware request within established service levels 90.00% of the time.)

Japan Machine Tool Orders Received: Sales data was reported at 147,782.000 JPY mn in Jun 2018. This records an increase from the previous number of 122,733.000 JPY mn for May 2018. Japan Machine Tool Orders Received: Sales data is updated monthly, averaging 107,425.000 JPY mn from Apr 2006 (Median) to Jun 2018, with 147 observations. The data reached an all-time high of 204,615.000 JPY mn in Mar 2018 and a record low of 33,511.000 JPY mn in Oct 2009. Japan Machine Tool Orders Received: Sales data remains active status in CEIC and is reported by Japan Machine Tool Builders' Association. The data is categorized under Global Database’s Japan – Table JP.C057: Machinery Tools Order Received.

The statistic depicts the objectives French business had set themselves with the use of Artificial Intelligence (AI) for their digital strategies in 2019. Most of the objectives were linked to improving their sales: The use of AI was seen as useful in predicting behavior (61 percent), to segment customers into groups in order to personalize marketing messages (50 percent), to manage the customer database as well as to model the purchasing behavior (both at 46 percent).

The generated dataset provides data on the relationships between customers in order to build part of the social graph of the bank. The data was collected from the logs of connections to the online banking of CaixaBank customers and stored in the entity’s Datapool for security and fraud prevention reasons. More concretely the data is coming from a set of restricted tables (relational database), with information related to the customers and their IP address when connecting online, used afterwards to see potential relationships between users’ and skip or enhance the security controls on eventual bank transfers between the connected users.

The Digital Basic Landscape Model (ATKIS Base DLM) is a digital, object-structured vector dataset. It determines the topographical objects of the real world by location and shape, names and characteristics. Furthermore, object-related material data are linked in such a way that the database can be used in a GIS application. In order to achieve a nationwide content uniformity of the data, the basic DLM is defined by means of an object type catalog derived from the AAA application scheme (ATKIS-OK Basic DLM), which contains regulations on the content and modelling of topographic information for the AdV basic data base and the country solutions. In addition to the objects of the object category groups ‘Siedlung’, ‘Transport’, ‘Vegetation’, ‘Waters’, ‘Administrative territorial units’ and ‘Relief Forms’, the contents also include structures and facilities on settlement areas and for traffic, as well as specific information on the waters. The position accuracy for the main linear objects (road axes, road axes, railway lines and water axes) is ± 3 m. When using a database, the ATKIS data can be submitted either completely or as user-related inventory data update (NBA) according to the customer’s desired time cycles. The data is provided free of charge via automated procedures or by self-collection. When using the data, the license conditions must be observed. The Digital Basic Landscape Model (ATKIS Base DLM) is a digital, object-structured vector dataset. It determines the topographical objects of the real world by location and shape, names and characteristics. Furthermore, object-related material data are linked in such a way that the database can be used in a GIS application. In order to achieve a nationwide content uniformity of the data, the basic DLM is defined by means of an object type catalog derived from the AAA application scheme (ATKIS-OK Basic DLM), which contains regulations on the content and modelling of topographic information for the AdV basic data base and the country solutions. In addition to the objects of the object category groups ‘Siedlung’, ‘Transport’, ‘Vegetation’, ‘Waters’, ‘Administrative territorial units’ and ‘Relief Forms’, the contents also include structures and facilities on settlement areas and for traffic, as well as specific information on the waters. The position accuracy for the main linear objects (road axes, road axes, railway lines and water axes) is ± 3 m. When using a database, the ATKIS data can be submitted either completely or as user-related inventory data update (NBA) according to the customer’s desired time cycles. The data is provided free of charge via automated procedures or by self-collection. When using the data, the license conditions must be observed.