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

    【Schema】
    【DB_ID】 movies_4
【Schema】
# Table: language_role
[
(role_id:INTEGER, Primary Key, Examples: [1, 2]),
(language_role:TEXT, Examples: [Original, Spoken])
]
# Table: country
[
(country_id:INTEGER, Primary Key, Examples: [128, 129, 130]),
(country_iso_code:TEXT, Examples: [AE, AF, AO]),
(country_name:TEXT, Examples: [United Arab Emirates, Afghanistan, Angola])
]
# Table: production_country
[
(movie_id:INTEGER, Examples: [5, 11, 12]),
(country_id:INTEGER, Examples: [214, 131, 152])
]
# Table: department
[
(department_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(department_name:TEXT, Examples: [Camera, Directing, Production])
]
# Table: movie_keywords
[
(movie_id:INTEGER, Examples: [5, 11, 12]),
(keyword_id:INTEGER, Examples: [612, 613, 616])
]
# Table: movie_company
[
(movie_id:INTEGER, Examples: [5, 11, 12]),
(company_id:INTEGER, Examples: [14, 59, 1])
]
# Table: language
[
(language_id:INTEGER, Primary Key, Examples: [24574, 24575, 24576]),
(language_code:TEXT, Examples: [en, sv, de]),
(language_name:TEXT, Examples: [English, svenska, Deutsch])
]
# Table: movie_cast
[
(movie_id:INTEGER, Examples: [285, 559, 767]),
(person_id:INTEGER, Examples: [85, 114, 116]),
(character_name:TEXT, Examples: [Captain Jack Sparrow, Will Turner, Elizabeth Swann]),
(gender_id:INTEGER, Examples: [2, 1, 0]),
(cast_order:INTEGER, Examples: [0, 1, 2])
]
# Table: movie_crew
[
(movie_id:INTEGER, Examples: [285, 559, 767]),
(person_id:INTEGER, Examples: [120, 1704, 770]),
(department_id:INTEGER, Examples: [1, 2, 3]),
(job:TEXT, Examples: [Director of Photography])
]
# Table: person
[
(person_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(person_name:TEXT, Examples: [George Lucas, Mark Hamill, Harrison Ford])
]
# Table: keyword
[
(keyword_id:INTEGER, Primary Key, Examples: [30, 65, 74]),
(keyword_name:TEXT, Examples: [individual, holiday, germany])
]
# Table: production_company
[
(company_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(company_name:TEXT, Examples: [Lucasfilm])
]
# Table: genre
[
(genre_id:INTEGER, Primary Key, Examples: [12, 14, 16]),
(genre_name:TEXT, Examples: [Adventure, Fantasy, Animation])
]
# Table: gender
[
(gender_id:INTEGER, Primary Key, Examples: [0, 1, 2]),
(gender:TEXT, Examples: [Unspecified, Female, Male])
]
# Table: movie_languages
[
(movie_id:INTEGER, Examples: [5, 11, 12]),
(language_id:INTEGER, Examples: [24574, 24575, 24576]),
(language_role_id:INTEGER, Examples: [2, 1])
]
# Table: movie
[
(movie_id:INTEGER, Primary Key, Examples: [5, 11, 12]),
(title:TEXT, Examples: [Four Rooms, Star Wars, Finding Nemo]),
(budget:INTEGER, Examples: [4000000, 11000000, 94000000]),
(homepage:TEXT),
(overview:TEXT),
(popularity:REAL, Examples: [22.87623, 126.393695, 85.688789]),
(release_date:DATE, Examples: [1995-12-09]),
(revenue:INTEGER, Examples: [4300000, 775398007, 940335536]),
(runtime:INTEGER, Examples: [98, 121, 100]),
(movie_status:TEXT, Examples: [Released, Rumored, Post Production]),
(tagline:TEXT),
(vote_average:REAL, Examples: [6.5, 8.1, 7.6]),
(vote_count:INTEGER, Examples: [530, 6624, 6122])
]
# Table: movie_genres
[
(movie_id:INTEGER, Examples: [5, 11, 12]),
(genre_id:INTEGER, Examples: [35, 80, 12])
]
【Foreign keys】
production_country.country_id=country.country_id
production_country.movie_id=movie.movie_id
movie_keywords.keyword_id=keyword.keyword_id
movie_keywords.movie_id=movie.movie_id
movie_company.company_id=production_company.company_id
movie_company.movie_id=movie.movie_id
movie_cast.gender_id=gender.gender_id
movie_cast.movie_id=movie.movie_id
movie_cast.person_id=person.person_id
movie_crew.department_id=department.department_id
movie_crew.movie_id=movie.movie_id
movie_crew.person_id=person.person_id
movie_languages.language_id=language.language_id
movie_languages.movie_id=movie.movie_id
movie_languages.language_role_id=language_role.role_id
movie_genres.genre_id=genre.genre_id
movie_genres.movie_id=movie.movie_id

    【Question】
    Provide the production companies of the movie that has over 35% average running time per movie in 2016. production companies refers to company_name; in 2016 refers to release_date LIKE '2016%'; over 35% average running time per movie refers to divide(subtract(runtime, AVG(runtime)), AVG(runtime)) * 100 as percentage > 35

    【Evidence】
    production companies refers to company_name; in 2016 refers to release_date LIKE '2016%'; over 35% average running time per movie refers to divide(subtract(runtime, AVG(runtime)), AVG(runtime)) * 100 as percentage > 35

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

SQL: SELECT T.company_name FROM ( SELECT DISTINCT T3.company_name, T1.runtime FROM movie AS T1 INNER JOIN movie_company AS T2 ON T1.movie_id = T2.movie_id INNER JOIN production_company AS T3 ON T3.company_id = T2.company_id WHERE T1.release_date LIKE '2016%' ) T WHERE T.runtime * 100 > (0.35 * ( SELECT AVG(T1.runtime) FROM movie AS T1 INNER JOIN movie_company AS T2 ON T1.movie_id = T2.movie_id INNER JOIN production_company AS T3 ON T3.company_id = T2.company_id WHERE T1.release_date LIKE '2016%' ) + ( SELECT AVG(T1.runtime) FROM movie AS T1 INNER JOIN movie_company AS T2 ON T1.movie_id = T2.movie_id INNER JOIN production_company AS T3 ON T3.company_id = T2.company_id WHERE T1.release_date LIKE '2016%' )) * 100