1. Objective: We want to detect outliers in bitcoin transactions that may reflect unusual activity.

2. License: Free to use but requires acknowledgement of http://compbio.cs.uic.edu

3. Data Source: http://compbio.cs.uic.edu/data/bitcoin/

4. DataSet Info: We have retained the first 100k records from the original dataset that contained 37.5 million records.

5. Field Meanings:
	A. transaction_id: Bitcoin transaction ID. Note that it is not a primary key.
	B. user_id_from: ID of user who sent the bitcoins.
	C. user_id_to: ID of user who received the bitcoins.
	D. date: When the transaction occurred.
	E. value: Amount of bitcoins being transferred.
	F. isOutlier: 1 (Outlier), 0 (Normal)

6. Parameter Selection:
	A. Dashboard Usage: Detect Categorical Outlier
	Settings:
	   1) Search command: | inputlookup bitcoin_transactions.csv | eval from_user = "u".user_id_from | eval to_user = "u".user_id_to
	   2) Field(s) to analyze: from_user, to_user, value