The Northwind database is a sample database that was originally created by Microsoft and used as the basis for their tutorials in a variety of database products for decades. The Northwind database contains the sales data for a fictitious company called “Northwind Traders,” which imports and exports specialty foods from around the world. The Northwind database is an excellent tutorial schema for a small-business ERP, with customers, orders, inventory, purchasing, suppliers, shipping, employees, and single-entry accounting. The Northwind database has since been ported to a variety of non-Microsoft databases, including PostgreSQL.

The Northwind dataset includes sample data for the following.

• Suppliers: Suppliers and vendors of Northwind
• Customers: Customers who buy products from Northwind
• Employees: Employee details of Northwind traders
• Products: Product information
• Shippers: The details of the shippers who ship the products from the traders to the end-customers
• Orders and Order_Details: Sales Order transactions taking place between the customers & the company

The HSE Books Customer Database, held by HSE’s appointed storage and Distribution services provider holds the following information: There are 19,228 customer records (256 Active and 18,972 closed) documenting orders placed from 1998 to date (31 July 2013). The orders relate to requests for printed copies of HSE’s guidance portfolio. These records include data on: Customer Address details;Standard Industry Classification code (SIC) where applicable; Type of business; Number of Employees; Order history; Payment history; Payment type; Credit limit; This information is provided to HSE for management information purposes. Sensitive information in relation to payments or bank account details is not shared with HSE and is dealt with under the appropriate financial controls operated by our service provider and the Data Protection Act.

Download: SQL Query This SQL project is focused on analyzing sales data from a relational database to gain insights into customer behavior, store performance, product sales, and the effectiveness of sales representatives. By executing a series of complex SQL queries across multiple tables, the project aggregates key metrics, such as total units sold and total revenue, and links them with customer, store, product, and staff details.

Key Objectives:

Customer Analysis: Understand customer purchasing patterns by analyzing the total number of units and revenue generated per customer. Product and Category Insights: Evaluate product performance and its category’s impact on overall sales. Store Performance: Identify which stores generate the most revenue and handle the highest sales volume. Sales Representative Effectiveness: Assess the performance of sales representatives by linking sales data with each representative’s handled orders. Techniques Used:

SQL Joins: The project integrates data from multiple tables, including orders, customers, order_items, products, categories, stores, and staffs, using INNER JOIN to merge information from related tables. Aggregation: SUM functions are used to compute total units sold and revenue generated by each order, providing valuable insights into sales performance. Grouping: Data is grouped by order ID, customer, product, store, and sales representative, ensuring accurate and summarized sales metrics. Use Cases:

Business Decision-Making: The analysis can help businesses identify high-performing products and stores, optimize inventory, and evaluate the impact of sales teams. Market Segmentation: Segment customers based on geographic location (city/state) and identify patterns in purchasing behavior. Sales Strategy Optimization: Provide recommendations to improve sales strategies by analyzing product categories and sales rep performance.

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.

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.

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.

United States Retail Sales: sa: NR: ow: Electronic Shopping and Mail Order Houses data was reported at 49.108 USD bn in May 2018. This records an increase from the previous number of 48.787 USD bn for Apr 2018. United States Retail Sales: sa: NR: ow: Electronic Shopping and Mail Order Houses data is updated monthly, averaging 13.986 USD bn from Jan 1992 (Median) to May 2018, with 317 observations. The data reached an all-time high of 49.108 USD bn in May 2018 and a record low of 2.477 USD bn in Mar 1992. United States Retail Sales: sa: NR: ow: Electronic Shopping and Mail Order Houses data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System.

This dataset provides a comprehensive overview of online sales transactions across different product categories. Each row represents a single transaction with detailed information such as the order ID, date, category, product name, quantity sold, unit price, total price, region, and payment method.

Columns:

• Order ID: Unique identifier for each sales order.
• Date:Date of the sales transaction.
• Category:Broad category of the product sold (e.g., Electronics, Home Appliances, Clothing, Books, Beauty Products, Sports).
• Product Name:Specific name or model of the product sold.
• Quantity:Number of units of the product sold in the transaction.
• Unit Price:Price of one unit of the product.
• Total Price: Total revenue generated from the sales transaction (Quantity * Unit Price).
• Region:Geographic region where the transaction occurred (e.g., North America, Europe, Asia).
• Payment Method: Method used for payment (e.g., Credit Card, PayPal, Debit Card).

Insights:

• 1. Analyze sales trends over time to identify seasonal patterns or growth opportunities.
• 2. Explore the popularity of different product categories across regions.
• 3. Investigate the impact of payment methods on sales volume or revenue.
• 4. Identify top-selling products within each category to optimize inventory and marketing strategies.
• 5. Evaluate the performance of specific products or categories in different regions to tailor marketing campaigns accordingly.

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.

The Hotel Room Booking & Customer Orders Dataset This is a rich, synthetic dataset meticulously designed for data analysts, data scientists, and machine learning practitioners to practice their skills on realistic e-commerce data. It models a hotel booking platform, providing a comprehensive and interconnected environment to analyze booking trends, customer behavior, and operational patterns. It is an ideal resource for building a professional portfolio project from initial exploratory data analysis to advanced predictive modeling.

The dataset is structured as a relational database, consisting of three core tables that can be easily joined:

rooms.csv: This table serves as the hotel's inventory, containing a catalog of unique rooms with essential attributes such as room_id, type, capacity, and price_per_night.

customers.csv: This file provides a list of unique customers, offering demographic insights with columns like customer_id, name, country, and age. This data can be used to segment customers and personalize marketing strategies.

orders.csv: As the central transactional table, it links rooms and customers, capturing the details of each booking. Key columns include order_id, customer_id, room_id, booking_date, and the order_total, which can be derived from the room price and the duration of the stay.

This dataset is valuable because its structure enables a wide range of analytical projects. The relationships between tables are clearly defined, allowing you to practice complex SQL joins and data manipulation with Pandas. The presence of both categorical data (room_type, country) and numerical data (age, price) makes it versatile for different analytical approaches.

Use Cases for Data Exploration & Modeling This dataset is a versatile tool for a wide range of analytical projects:

Data Visualization: Create dashboards to analyze booking trends over time, identify the most popular room types, or visualize the geographical distribution of your customer base.

Machine Learning: Build a regression model to predict the order_total based on room type and customer characteristics. Alternatively, you could develop a model to recommend room types to customers based on their past orders.

SQL & Database Skills: Practice complex queries to find the average order value per country, or identify the most profitable room types by month.

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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 (** percent), to segment customers into groups in order to personalize marketing messages (** percent), to manage the customer database as well as to model the purchasing behavior (both at ** percent).

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.)

In recent times, it has become necessary for any business to have an online presence in order to remain relevant and competitive. As a result of this necessity many businesses, including small enterprises, now operate an e-commerce web store so as to increase sales and attract new customers. Also, business owners do not have to worry about finding a place to erect their stores and customers can have unhindered access to a wide range of products at any time and anywhere in the world. The objective of this thesis project was to develop an e-commerce Java web application for a small retail store where the store owner sells his/her products online. The application allows the owner to manage products, customers, and orders. Also, with the application customers make orders and pay for the ordered products. The application uses PayPal Express Checkout as its payment solution. In addition, the web store offers customers and visitors to the site an opportunity to subscribe to an email list in order to get news about new products and special offers. Lastly, the application sends an automatic email confirmation after completing an order or subscribing to an email list. The development of this application was carried out on Eclipse IDE using the Java programming language. The database communication of the application was implemented by using JPA and JPQL, and MySQL database was used to store the application data. The application was structured according to the Model-View-Controller (MVC) pattern. The model, the view and the controller layers were implemented by using JavaBeans, JSPs, and Servlet API respectively. The payment transaction of the application was carried out on PayPal Sandbox (testing environment) with different NVP API operations.

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 synthetically generated based on real data coming from a set of restricted tables (relational database), with information related to the customers and their IP address when connecting online.

CAIXA and IBM generated the data recipe for the data fabrication using IBM TDF. Through an iterative analysis of obtained results, the rules were improved in order to obtain the fabricated dataset used for testing the MVP, with more than 1 million entries.

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.

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.