Donated on 7/6/1999

This data set contains a list of over 10000 films including many older, odd, and cult films. There is information on actors, casts, directors, producers, studios, etc.

Dataset Characteristics

Multivariate, Relational

Subject Area


Associated Tasks


Feature Type


# Instances


# Features


Dataset Information

Additional Information

The data is stored in relational form across several files. The central file (MAIN) is a list of movies, each with a unique identifier. These identifiers may change in successive versions. The actors (CAST) for those movies are listed with their roles in a distinct file. More information about individual actors (ACTORS) is in a third file. All directors in MAIN are listed in a fourth file (PEOPLE), with a number of important producers, writers, and cinematographers. A fifth file (REMAKES) links movies that were copied to a substantial extent from each other. The sixth file (STUDIOS) provides some information about studios shown in MAIN. The original motivation was for database class exercises, to replace the boring `manager of the toy-department' queries. Note that the CASTS, refering MAIN and ACTORS is logically identical to the inventory file refering to suppliers and assemblies in the the standard bill-of-materials problems. Personal interests caused the database to be made complete for all Hitchcock movies and TV episodes. Related films by type and actor were added gradually. Subsequent research on temporal databases caused date fields (years only) to be added. It allows testing, say, if the dates-of-work of an ACTOR match the dates of the MAIN films that the CAST relation shows. Object-oriented database features could be tested with fields having multiple and two-level values, as documented in DOC. The entries were gradually collected during course work starting about 1975 and are still being updated. Most of the entries were manual. The DOC file lists some of the reference works used. Corrections and additions continue to be appreciated. Detailed descriptions of the fields and their formats is provided in doc.html. Missing Values: Outside of key fields, missing values are common. Their encoding is described in DOC. Sometimes the data seems to be unavailable, sometimes it hasn't been entered. Some information, as `lived-with' is inherently incomplete. Censored Data: Minor actors are ignored. Dependencies: Every MAIN film must have a director in PEOPLE. About 50 pseudo director names ahve been listed in PEOPLE to allow interesting films to with (yet) unknown directors to be entered. Every CASTS entry must relate to a MAIN film entry. Every ACTOR should appear in some CASTS entry, but not vice versa. See DOC for more type information. Other Relevant Information: Films are listed, if known, with their original language title. An Alt(T: ) field provides English translations, where known. Data Format: The current files are in HTML, to allow easy parsing to other formats. An XML version is being considered. The approximate file sizes are: DOC ....... 50K MAIN ...... 1 145K 11 400 entries PEOPLE .... 355K 3 290 entries CASTS ..... 4 340K 46 000 entries ACTORS .... 811K 6 800 entries REMAKES ... 135K 1 278 entries STUDIOS ... 26K 200 entries

Has Missing Values?


Papers Citing this Dataset

CrossTrainer: Practical Domain Adaptation with Loss Reweighting

By Justin Chen, Edward Gan, Kexin Rong, Sahaana Suri, Peter Bailis. 2019

Published in ArXiv.

Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model

By Wenbo Gong, Sebastian Tschiatschek, Richard Turner, Sebastian Nowozin, Jos'e Hern'andez-Lobato, Cheng Zhang. 2019

Published in ArXiv.

Distributing Frank-Wolfe via Map-Reduce

By Armin Moharrer, Stratis Ioannidis. 2018

Published in IJCAI.

Nonnegative matrix factorization with side information for time series recovery and prediction

By Jiali Mei, Yohann Castro, Yannig Goude, Jean-Marc Azais, Georges H'ebrail. 2017

Published in IEEE Transactions on Knowledge and Data Engineering.

A Scale Mixture Perspective of Multiplicative Noise in Neural Networks

By Eric Nalisnick, Anima Anandkumar, Padhraic Smyth. 2015

Published in

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6 citations


Gio Wiederhold


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