You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 bike_share_1
【Schema】
# Table: station
[
(id:INTEGER, Primary Key, Examples: [2, 3, 4]),
(name:TEXT, Examples: [San Jose Diridon Caltrain Station]),
(lat:REAL, Examples: [37.329732, 37.330698, 37.333988]),
(long:REAL, Examples: [-121.90178200000001, -121.888979, -121.894902]),
(dock_count:INTEGER, Examples: [27, 15, 11]),
(city:TEXT, Examples: [San Jose, Redwood City, Mountain View]),
(installation_date:TEXT, Examples: [8/6/2013, 8/5/2013, 8/7/2013])
]
# Table: status
[
(station_id:INTEGER, Examples: [2, 3, 4]),
(bikes_available:INTEGER, Examples: [2, 3, 4]),
(docks_available:INTEGER, Examples: [25, 24, 23]),
(time:TEXT, Examples: [2013/08/29 12:06:01, 2013/08/29 12:07:01, 2013/08/29 12:08:01])
]
# Table: trip
[
(id:INTEGER, Primary Key, Examples: [4069, 4073, 4074]),
(duration:INTEGER, Examples: [174, 1067, 1131]),
(start_date:TEXT, Examples: [8/29/2013 9:08, 8/29/2013 9:24, 8/29/2013 9:25]),
(start_station_name:TEXT, Examples: [2nd at South Park]),
(start_station_id:INTEGER, Examples: [64, 66, 22]),
(end_date:TEXT, Examples: [8/29/2013 9:11, 8/29/2013 9:42, 8/29/2013 9:43]),
(end_station_name:TEXT, Examples: [2nd at South Park]),
(end_station_id:INTEGER, Examples: [64, 69, 22]),
(bike_id:INTEGER, Examples: [288, 321, 317]),
(subscription_type:TEXT, Examples: [Subscriber, Customer]),
(zip_code:INTEGER, Examples: [94114, 94703, 94115])
]
# Table: weather
[
(date:TEXT, Examples: [8/29/2013, 8/30/2013, 8/31/2013]),
(max_temperature_f:INTEGER, Examples: [74, 78, 71]),
(mean_temperature_f:INTEGER, Examples: [68, 69, 64]),
(min_temperature_f:INTEGER, Examples: [61, 60, 57]),
(max_dew_point_f:INTEGER, Examples: [61, 57, 60]),
(mean_dew_point_f:INTEGER, Examples: [58, 56, 60]),
(min_dew_point_f:INTEGER, Examples: [56, 54, 53]),
(max_humidity:INTEGER, Examples: [93, 90, 87]),
(mean_humidity:INTEGER, Examples: [75, 70, 68]),
(min_humidity:INTEGER, Examples: [57, 50, 49]),
(max_sea_level_pressure_inches:REAL, Examples: [30.07, 30.05, 30.0]),
(mean_sea_level_pressure_inches:REAL, Examples: [30.02, 30.0, 29.96]),
(min_sea_level_pressure_inches:REAL, Examples: [29.97, 29.93, 29.92]),
(max_visibility_miles:INTEGER, Examples: [10, 9, 8]),
(mean_visibility_miles:INTEGER, Examples: [10, 9, 8]),
(min_visibility_miles:INTEGER, Examples: [10, 7, 6]),
(max_wind_Speed_mph:INTEGER, Examples: [23, 29, 26]),
(mean_wind_speed_mph:INTEGER, Examples: [11, 13, 15]),
(max_gust_speed_mph:INTEGER, Examples: [28, 35, 31]),
(precipitation_inches:TEXT, Examples: [0, 0.23, T]),
(cloud_cover:INTEGER, Examples: [4, 2, 6]),
(events:TEXT, Examples: [Fog, Rain, Fog-Rain]),
(wind_dir_degrees:INTEGER, Examples: [286, 291, 284]),
(zip_code:TEXT, Examples: [94107, 94063, 94301])
]

    【Question】
    Which day in the month of November, 2014 have a foggy weather in the zip code 94301 and in total, how many bikes were borrowed by subscribers from all of the stations in the said day? day in the month of November, 2014 refers to start_date between '11/1/2014' and '11/30/2014'; foggy weather refers to events = 'Fog'; subscriber refers to subscription_type; all of the stations bikes were borrowed from refer to start_station_name;

    【Evidence】
    day in the month of November, 2014 refers to start_date between '11/1/2014' and '11/30/2014'; foggy weather refers to events = 'Fog'; subscriber refers to subscription_type; all of the stations bikes were borrowed from refer to start_station_name;

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T2.date, COUNT(T1.start_station_name) FROM trip AS T1 INNER JOIN weather AS T2 ON T2.zip_code = T1.zip_code WHERE T2.date LIKE '11/%/2014%' AND T2.zip_code = 94301 AND T2.events = 'Fog' AND T1.subscription_type = 'Subscriber'