add IBM Anti Money Laundering (aml) dataset

data/aml/AML_Trans.schema

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+<Account.bank>: uid .
+<Account.id>: string @index(hash) .
+<Bank.id>: string @index(hash) .
+<Transaction.amount_paid>: float  @index(float) .
+<Transaction.amount_received>: float   @index(float) .
+<Transaction.from>: uid @reverse .
+<Transaction.payment_currency>: string .
+<Transaction.payment_type>: string  @index(hash) .
+<Transaction.receiving_currency>: string .
+<Transaction.to>: uid @reverse .
+<Transaction.timestamp>: datetime @index(day) .
+<Transaction.laundering>:  bool @index(bool) .
+<Transaction.id>: string @index(hash) .
+
+<xid>: string @index(exact) @upsert .
+
+type <Bank> {
+	<Bank.id>
+}
+
+type <Account> {
+    <Account.id>
+    <Account.bank>
+}
+
+type <Transaction> {
+    <Transaction.id>
+    <Transaction.timestamp>
+    <Transaction.amount_paid>
+    <Transaction.amount_received>
+    <Transaction.from>
+    <Transaction.payment_currency>
+    <Transaction.payment_type>
+    <Transaction.receiving_currency>
+    <Transaction.to>
+    <Transaction.laundering>
+}

data/aml/HI-Small_Trans.template

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+# Timestamp,From Bank,Account,To Bank,Account,Amount Received,Receiving Currency,Amount Paid,Payment Currency,Payment Format,Is Laundering
+
+<_:Bank_[From Bank]> <dgraph.type> "Bank" .
+<_:Bank_[From Bank]> <Bank.id> "[From Bank]" .
+<_:Bank_[To Bank]> <dgraph.type> "Bank" .
+<_:Bank_[To Bank]> <Bank.id> "[To Bank]" .
+<_:A_[From Account]> <dgraph.type> "Account" .
+<_:A_[From Account]> <Account.id> "[From Account]" .
+<_:A_[From Account]> <Account.bank> <_:Bank_[From Bank]> .
+<_:A_[To Account]> <dgraph.type> "Account" .
+<_:A_[To Account]> <Account.id> "[To Account]" .
+<_:A_[To Account]> <Account.bank> <_:Bank_[To Bank]> .
+<_:T_[LINENUMBER]> <Transaction.id> "[Timestamp],[From Bank],[From Account]" .
+<_:T_[LINENUMBER]> <dgraph.type> "Transaction" .
+<_:T_[LINENUMBER]> <Transaction.from> <_:A_[From Account]> .
+<_:T_[LINENUMBER]> <Transaction.to> <_:A_[To Account]> .
+<_:T_[LINENUMBER]> <Transaction.timestamp> "=datetime([Timestamp],%Y/%m/%d %H:%M)" .
+<_:T_[LINENUMBER]> <Transaction.amount_received> "[Amount Received]" .
+<_:T_[LINENUMBER]> <Transaction.receiving_currency> "[Receiving Currency]" .
+<_:T_[LINENUMBER]> <Transaction.amount_paid> "[Amount Paid]" .
+<_:T_[LINENUMBER]> <Transaction.payment_currency> "[Payment Currency]" .
+<_:T_[LINENUMBER]> <Transaction.payment_type> "[Payment Format]" .
+<_:T_[LINENUMBER]> <Transaction.laundering> "[Is Laundering]" .

data/aml/README.md

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+
+IBM Transactions for Anti Money Laundering (AML)
+from 
+https://www.kaggle.com/datasets/ealtman2019/ibm-transactions-for-anti-money-laundering-aml
+
+The folder contains a schema file and RDF files created from 
+HI-Small_Trans.csv
+
+RDF file has been created using [csv_to_rdf](https://github.com/hypermodeinc/dgraph-experimental/blob/main/data-import/csv-to-rdf/csv_to_rdf.py) python script from 
+dgraph experimental repository, using the template file provided in this folder.
+
+See the corresponding HI_Small_Patterns.tx file for generated fraudulent transactions. These transactions are labeled as Transaction.laundering true in the dataset.
+
+