This dataset is from source, which contains 11,341 individuals’ information, based on the popularity of their biographical page in Wikipedia.
| Attribute Details |
|---|
| article_id |
| full_name |
| sex |
| birth_year |
| city: The city in which the figure was primarily active. |
| state: The state (if applicable) in which the figure was primarily active. |
| country: The country in which the figure was primarily active. |
| continent: The continent in which the figure was primarily active. |
| latitude: The central latitude of the city, state, etc. that the figure was active in. |
| longitude: The central longitude of the city, state, etc. that the figure was active in. |
| occupation: The historical figure’s specific occupation. E.g. physicist. |
| industry: The industry (or topic area) the figure concentrated their work in. E.g. philosophy. |
| domain: The general area of contribution the figure is known for. E.g. humanities. |
| article_languages: How many of the different language Wikipedias have an article on the figure. |
| page_views: The estimated total page views for the figure across all Wikipedias. |
| average_views: The estimated average page views for the figure per each Wikipedia edition article. |
| historical_popularity_index: An index value measuring approximately the popularity |
The dataset contains 300K records with 40 attributes from the U.S. Census Bureau. This dataset can be found here, containing population data.
| Attribute Details |
|---|
| id: record id |
| age: continuous. |
| workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked. |
| education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, |
| Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool. |
| education-num: continuous. |
| marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse. |
| occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, |
| Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces. |
| relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried. |
| race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black. |
| sex: Female, Male. |
| capital-gain: continuous. |
| capital-loss: continuous. |
| hours-per-week: continuous. |
| native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands. |
This dataset can be found here source, which contains individuals’ credit report information.
| Attribute Details |
|---|
| Age |
| Sex |
| Job: unskilled and non-resident, unskilled and resident, skilled, high |
| Housing: own, rent, free |
| Saving accounts: little, moderate, quite rich, rich |
| Checking account |
| Credit amount |
| Duration |
| Purpose: car, furniture/equipment, radio/TV, domestic appliances, repairs, education, business, vacation/others |
We generate a synthetic dataset describing population characteristics (age, education, race, gender, income, marital status, occupation). We use the Synner.io tool to declaratively specify the distribution properties in the characteristic attributes. The size of generated is 100,000. The distribution we generated are uniform, Gaussian (mean = 0, with 1 standard deviation), and Zipfian (alpha = 1).
Given a diversity constraint, it has four components: attribute name, attribute value, the frequency of the attribute value (lower bound) and the frequency of the attribute value (lower bound). We denote in this format attrName(attrValue)[lower bound, upper bound]. For example, city(New York) [100, 400] means the attribute name is city, the attribute value New York. the lower bound 100 and upper bound 400.
Diversity constraints examples:
city(New York) [100, 400]
city(Paris) [100, 300]
city(Los Angeles) [100, 200]
country(United States) [100, 150]
country(United Kingdom) [100, 150]
country(France) [100, 150]
continent(Europe) [100, 150]
continent(North America) [100, 150]
occupation(Politician) [1000, 2500]
occupation(Actor) [600, 1500]
industry (Government) [1000, 2500]
industry (Film And Theatre) [600, 1500]
Diversity constraints on attributes Sex and Continent
Minimum:
Sex(Male) [1, 5636]
Sex(Female) [1, 1]
continent(Europe) [1, 5631]
continent(North America) [1, 1]
continent(Asia) [1, 1]
continent(Africa) [1, 1]
continent(South America) [1, 1]
continent(Oceania) [1, 1]
Average:
Sex(Male) [2818, 2818]
Sex(Female) [2818, 2818]
continent(Europe) [939, 2850]
continent(North America) [939, 939]
continent(Asia) [939, 939]
continent(Africa) [419, 419]
continent(South America) [366, 366]
continent(Oceania) [123, 123]
Proportion
Sex(Male) [4893, 4893]
Sex(Female) [742, 742]
continent(Europe) [3164, 3164]
continent(North America) [1212, 1212]
continent(Asia) [590, 590]
continent(Africa) [208, 208]
continent(South America) [181, 181]
continent(Oceania) [61, 61]
Diversity constraints on attributes Sex and Race
Minimum:
Sex(Male) [1, 286882]
Sex(Female) [1, 1]
race(White)[1, 286882]
race(Black)[1, 1]
race(Asian-Pac-Islander)[1, 1]
race(Amer-Indian-Eskimo)[1, 1]
race(Other)[1, 1]
Average:
Sex(Male) [217900, 286882]
Sex(Female) [107710, 143441]
race(White)[57376, 286882]
race(Black)[31240, 31240]
race(Asian-Pac-Islander)[10390, 10390]
race(Amer-Indian-Eskimo)[3110, 3110]
race(Other)[2710, 2710]
Proportion
sex(Male) [208868, 286882]
sex(Female) [103246, 143441]
race(White)[266631, 286882]
race(Black)[29945, 29945]
race(Asian-Pac-Islander)[9959, 9959]
race(Amer-Indian-Eskimo)[2981, 2981]
race(Other)[2598, 2598]
sex(Male)race(White)[186331, 286882]
sex(Male)race(Black)[19700, 19700]
sex(Male)race(Asian-Pac-Islander)[5411, 5411]
sex(Male)race(Amer-Indian-Eskimo)[1994, 1994]
sex(Male)race(Other)[1844, 1844]
sex(Female)race(White)[80300, 80300]
sex(Female)race(Black)[10245, 10245]
sex(Female)race(Asian-Pac-Islander)[4548, 4548]
sex(Female)race(Amer-Indian-Eskimo)[987, 987]
sex(Female)race(Other)[754, 754]
Diversity constraints on attributes Sex and Job
Minimum:
Sex(Male) [1, 690]
Sex(Female) [1, 1]
Job(skilled)[1, 630]
Job(unskilled and resident)[1, 1]
Job(high)[1, 1]
Job(unskilled and non-resident)[1,1]
Average:
Sex(Male) [500, 690]
Sex(Female) [310, 310]
Job(skilled)[250, 630]
Job(unskilled and resident)[200, 200]
Job(high)[148, 148]
Job(unskilled and non-resident)[22, 22]
Proportion
Sex(Male) [662, 690]
Sex(Female) [298, 298]
Job(skilled)[605, 605]
Job(unskilled and resident)[192, 192]
Job(high)[142, 142]
Job(unskilled and non-resident)[21, 21]
Diversity constraints on attributes Race and Gender.
sex(Male) [45000, 55000]
sex(Female) [45000, 55000]
race(White)[34000, 42000]
race(Black)[16000, 28000]
race(Asian-Pac-Islander)[8000, 14000]
race(Amer-Indian-Eskimo)[9000, 15000]
race(Other)[6000, 8000]
sex(Male)race(White)[25000, 32000]
To model diversity constraints, we define Q to be COUNT queries containing the desired characteristic attribute values.
For example, if the given diversity constraint is sex(Male) [208868, 286882], then the COUNT query will be select count(*) from Census where sex = "Male";
sex(Male) [208868, 286882]
sex(Female) [103246, 143441]
race(White)[266631, 286882]
race(Black)[29945, 29945]
race(Asian-Pac-Islander)[9959, 9959]
race(Amer-Indian-Eskimo)[2981, 2981]
race(Other)[2598, 2598]
sex(Male)race(White)[186331, 286882]
The source code is available here.