Natural Language Processing on Data Warehouses

The main problem addressed in this research was to use natural language to query data in a data warehouse. To this effect, two natural language processing models were developed and compared on a classic star-schema sales data warehouse with sales facts and date, location and item dimensions. Utterances are queries that people make with natural language, for example, What is the sales value for mountain bikes in Georgia for 1 July 2005?" The first model, the heuristics model, implemented an algorithm that steps through the sequence of utterance words and matches the longest number of consecutive words at the highest grain of the hierarchy. In contrast, the embedding model implemented the word2vec algorithm to create different kinds of vectors from the data warehouse. These vectors are aggregated and then the cosine similarity between vectors was used to identify concepts in the utterances that can be converted to a programming language. To understand question style, a survey was set up which then helped shape random utterances created to use for the evaluation of both methods. The first key insight and main premise for the embedding model to work is a three-step process of creating three types of vectors. The first step is to train vectors (word vectors) for each individual word in the data warehouse; this is called word embeddings. For instance, the word `bike' will have a vector. The next step is when the word vectors are averaged for each unique column value (column vectors) in the data warehouse, thus leaving an entry like `mountain bike' with one vector which is the average of the vectors for `mountain' and `bike'. Lastly, the utterance by the user is averaged (utterance vectors) by using the word vectors created in step one, and then, by using cosine similarity, the utterance vector is matched to the closest column vectors in order to identify data warehouse concepts in the utterance. The second key insight was to train word vectors firstly for location, then separately for item - in other words, per dimension (one set for location, and one set for item). Removing stop words was the third key insight, and the last key insight was to use Global Vectors to instantiate the training of the word vectors. The results of the evaluation of the models indicated that the embedding model was ten times faster than the heuristics model. In terms of accuracy, the embedding algorithm (95.6% accurate) also outperformed the heuristics model (70.1% accurate). The practical application of the research is that these models can be used as a component in a chatbot on data warehouses. Combined with a Structured Query Language query generation component, and building Application Programming Interfaces on top of it, this facilitates the quick and easy distribution of data; no knowledge of a programming language such as Structured Query Language is needed to query the data.

Grantor

Department of Statistical Sciences

Year dc:date.issued

2019

Author dc:creator

• Maree, Stiaan

Advisor dc:contributor.advisor

• Durbach, Ian

• Data Science

Handle dc:identifier.uri

http://hdl.handle.net/11427/36903

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/36903