Eluslooduse klassifikatsioonide haldamise andmebaas ja veebiliides
Date
2015
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Abstract
Käesolevas töös käsitletakse eluslooduse klassifikatsioone haldavaid tarkvarasid ning arendatakse veebiteenus bioloogiliste taksonoomiate paremaks haldamiseks. Töö annab ülevaate eluslooduse klassifikatsioonide arendamisega seotud terminoloogiast ja protsessist ning võrdleb olemasolevaid haldusliideseid.
Töös kirjeldatakse erinevaid võimalusi hierarhiliste andmete talletamiseks relatsioonilises andmebaasis ning valitakse sobivaim viis muutliku eluslooduse klassifikatsiooni talletamiseks. Valitud andmebaasi disainimustrit kasutatakse REST stiilis veebiliidese arendamisel. Luuakse kõiki kasutajapoolseid nõuded täitev veebiteenus kasutades Django REST raamistikku.
Magistritöö ühe praktilise osana testitakse loodud veebiliidese enimkasutatud sihtpunktide kasutuskiirust erineva hulga algandmetega. Lisaks antakse soovitusi rakenduse kasutuskiiruse ja -mugavuse edasiseks parandamiseks.
This master's thesis reviews software that is built for managing biological classifications and builds a new web service for better management of multiple biological classifications. The thesis gives an overview about terminology and the process of developing biological classifications. The thesis describes different possibilities for storing hierarchical data in a relational database. The most suitable method is used for storing constantly changing biological classifications. The chosen database design pattern is used when building the REST web service with Django REST framework. The web service meets all client specified requirements. As part of the practical work, performance test are ran against most used API endpoints. The performance tests are ran with different sized classifications. Additionally the author gives recommendations for further improving user experience and module performance.
This master's thesis reviews software that is built for managing biological classifications and builds a new web service for better management of multiple biological classifications. The thesis gives an overview about terminology and the process of developing biological classifications. The thesis describes different possibilities for storing hierarchical data in a relational database. The most suitable method is used for storing constantly changing biological classifications. The chosen database design pattern is used when building the REST web service with Django REST framework. The web service meets all client specified requirements. As part of the practical work, performance test are ran against most used API endpoints. The performance tests are ran with different sized classifications. Additionally the author gives recommendations for further improving user experience and module performance.