Winner determination in huge elections with MapReduce
In computational social choice, we are concerned with the development of methods for joint decision making. A central problem in this field is the winner determination problem, which aims at identifying the most preferred alternative(s). With the rise of modern e-business platforms, processing of hu...
| Main Authors: | , , , |
|---|---|
| Format: | Conference item |
| Language: | English |
| Published: |
AAAI Press
2017
|
| _version_ | 1797057671298285568 |
|---|---|
| author | Csar, T Lackner, M Pichler, R Sallinger, E |
| author_facet | Csar, T Lackner, M Pichler, R Sallinger, E |
| author_sort | Csar, T |
| collection | OXFORD |
| description | In computational social choice, we are concerned with the development of methods for joint decision making. A central problem in this field is the winner determination problem, which aims at identifying the most preferred alternative(s). With the rise of modern e-business platforms, processing of huge amounts of preference data has become an issue. In this work, we apply the MapReduce framework – which has been specifically designed for dealing with big data – to various versions of the winner determination problem. We obtain efficient and highly parallel algorithms and provide a theoretical analysis and experimental evaluation. |
| first_indexed | 2024-03-06T19:39:43Z |
| format | Conference item |
| id | oxford-uuid:2041564b-12ec-41a6-be9c-d7769d93298e |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T19:39:43Z |
| publishDate | 2017 |
| publisher | AAAI Press |
| record_format | dspace |
| spelling | oxford-uuid:2041564b-12ec-41a6-be9c-d7769d93298e2022-03-26T11:26:26ZWinner determination in huge elections with MapReduceConference itemhttp://purl.org/coar/resource_type/c_5794uuid:2041564b-12ec-41a6-be9c-d7769d93298eEnglishSymplectic Elements at OxfordAAAI Press2017Csar, TLackner, MPichler, RSallinger, EIn computational social choice, we are concerned with the development of methods for joint decision making. A central problem in this field is the winner determination problem, which aims at identifying the most preferred alternative(s). With the rise of modern e-business platforms, processing of huge amounts of preference data has become an issue. In this work, we apply the MapReduce framework – which has been specifically designed for dealing with big data – to various versions of the winner determination problem. We obtain efficient and highly parallel algorithms and provide a theoretical analysis and experimental evaluation. |
| spellingShingle | Csar, T Lackner, M Pichler, R Sallinger, E Winner determination in huge elections with MapReduce |
| title | Winner determination in huge elections with MapReduce |
| title_full | Winner determination in huge elections with MapReduce |
| title_fullStr | Winner determination in huge elections with MapReduce |
| title_full_unstemmed | Winner determination in huge elections with MapReduce |
| title_short | Winner determination in huge elections with MapReduce |
| title_sort | winner determination in huge elections with mapreduce |
| work_keys_str_mv | AT csart winnerdeterminationinhugeelectionswithmapreduce AT lacknerm winnerdeterminationinhugeelectionswithmapreduce AT pichlerr winnerdeterminationinhugeelectionswithmapreduce AT sallingere winnerdeterminationinhugeelectionswithmapreduce |