A Spark-Based Intelligent Assistant - Making Data Exploration in Natural Language Real_iteblog

Rather than running pre-defined queries embedded in dashboards, business users and data scientists want to explore data in more intuitive ways. Natural language interfaces for data exploration have gained considerable traction in industry. Their success is triggered by advancements in machine learning and by novel big data technologies that enable processing large amounts of data in real-time. However, even though these systems show significant progress, they have not yet reached the maturity level to support real users in data exploration scenarios either due to the lack of supported functionality or the narrow application scope, remaining one of the ‘holy grails’ of the data analytics community.

In this talk, we will present a Spark-based architecture of an intelligent data assistant, a system that combines real-time data processing and analytics over large amounts of data with user interaction in natural language, and we will argue why Spark is the right platform for next-gen intelligent data assistants.

Our intelligent data assistant
(a) enables a more natural interaction with the user through natural language;
(b) offers active guidance through explanations and suggestions;
(c) constantly learns and improves its performance. To build an intelligent data assistant, there are several challenges. Unlike search engines, users tend to express sophisticated query logics and expect perfect results. The inherent complexity of natural languages complicates things in several ways. The intricacies of the data domain require that the system constantly expands its domain knowledge and its ability to interpret new data and user queries by constantly analyzing data and queries.

Our intelligent data assistant brings together several components, including natural language processing for understanding user queries and generating answers in natural language, automatic knowledge base construction techniques for learning about data sources and how to find the information requested, as well as deep learning methods for query disambiguation and domain understanding.

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1.WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics

2.A Spark-based Intelligent Assistant Making Data Exploration in Natural Language Real Georgia Koutrika, ATHENA Research Center #UnifiedDataAnalytics #SparkAISummit

3. Data, Data, Data Data growth More data than humans can process and comprehend Data democratization From scientists to the public, increasingly more users are consumers of data #UnifiedDataAnalytics #SparkAISummit 3

4.HOW CAN WE EXPLORE AND LEVERAGE OUR DATA? 4

5.Select p.status SQL Results queries from conference_attendees p where p.conference=‘SPARK+AI2019’ Data Store #UnifiedDataAnalytics #SparkAISummit 5

6.How are you today? #UnifiedDataAnalytics #SparkAISummit 6

7.The phases of data exploration 7

8. The “SQL” Age which cities have SELECT * FROM CITIES year-round average WHERE 50 < temperature above (SELECT AVG(TEMP_F) Period characteristics 50 degrees? FROM STATS WHERE CITIES.ID = STATS.ID); User type : - Sophisticated user Knowledge: - Precise knowledge of data and schema Info need: - Precise knowledge of their need Interaction: - User “speaks” fluently SQL - DBMS “responds” with tables Users Programmer DBMS Limited access (for most but the privileged) Communication bottleneck (the guru) Data starvation (for those that really need it) Limited interaction (query answering) #UnifiedDataAnalytics #SparkAISummit 8

9.The “Baby Talk” Age cities with year-round average temperature above 50 degrees? Period characteristics User type : - Domain Expert Knowledge: - User understands the data domain Info need: - Precise knowledge of their need Interaction: - User not familiar with SQL - DBMS “responds” with tables and graphs Business user DBMS User pretty much knows what to ask User queries are relatively simple Query answering paradigm (still) #UnifiedDataAnalytics #SparkAISummit 9

10.Chatbots A chatbot: • mimics conversations with people • uses artificial intelligence techniques • lives on consumer messaging platforms, as a means for consumers to interact with brands. Drawbacks • Primarily text interfaces based on rules • Encourage canned, linear-driven interactions • Deal with simple,https://medium.com/swlh/chatbots-of-the-future-86b5bf762bb4 unambiguous questions (“what is the weather forecast today”) • Cannot answer random or complex queries over data repositories #UnifiedDataAnalytics #SparkAISummit 10

11. Conversational AI For example, Google Duplex: demo released in May 2018 • The technology is directed towards completing specific tasks, such as scheduling certain types of appointments. • For such tasks, the system makes the conversational experience as natural as possible. • One of the key research insights was to constrain Duplex to closed domains. • Duplex can only carry out natural conversations after being deeply trained in such domains #UnifiedDataAnalytics #SparkAISummit 11

12.Human-like Data Exploration Requirements User type : - From expert to data consumer Knowledge: - Intuition about the data Info need: - Not necessarily sure what to ask Interaction: - Intuitive, natural, interaction #UnifiedDataAnalytics #SparkAISummit 12

13.Human-like Data Exploration An Intelligent Data Assistant • converses with the user in a more natural bilateral interaction; • actively guides the user offering explanations and suggestions; • keeps track of the context and can respond and adapt accordingly; • constantly improves its behavior by learning and adapting. #UnifiedDataAnalytics #SparkAISummit 13

14. Intelligent Data Assistant Syntax-based (NLP) Query Understanding Data-based Query Understanding NL Query Explanations Query Recommendations #UnifiedDataAnalytics #SparkAISummit 14

15. Step 1: Let the user ask Syntax-based (NLP) using natural language Query Understanding Data-based Query Understanding NL Query Explanations Query Recommendations

16.Facts Unlike search engines, users tend to express sophisticated query logics to a data assistant and expect perfect results Translating a natural language query to a structured query is hard! #UnifiedDataAnalytics #SparkAISummit 16

17. Challenges: from the NL Side Synonymy: multiple words with the same Paraphrasing Multiple way to say the same thing meaning e.g.,“movies" and “films" E.g. ‘how many people live in ..." could be a mention of the “Population“ column. Polysemy: a single word has multiple meanings e.g. Paris as in the city and Paris Hilton Context dependent terms: Syntactic: Multiple readings based on syntax E.g., “Return the top product" the term “top" is a modifier for e.g., “Find all German movie directors“ means: “product". Does it mean Based on popularity?? Based on “directors that have directed German movies" ?? number of sales?? “directors from Germany that have directed a movie“?? Elliptical queries: sentences from which one or Semantic: Multiple meanings for a sentence. more words are omitted. e.g. “Are Brad and Angelina married?". E.g. , “Return the movies of Clooney". Are they married to each other or separately. Non-exact matching: mentions do not map exactly to values or tables/attributes) E.g. Who is the best actress in 2011 à ‘actress’ should map to the “actor” column #UnifiedDataAnalytics #SparkAISummit 17

18. Challenges: from the Data Side Complex Syntax: SQL is a structured language with a strict grammar and limited expressivity when compared to natural language. e.g., “Return the movie with the best rating". Should look like “SELECT name , MAX( rating ) FROM Movie ;” but it is WAY more complicated Database Structure: E.g., for the term “date" a system may need to retrieve three attributes: year, month, day Multiple relationships: mentions may connect in multiple ways/join disambiguation E.g., “Woody Allen movies” may need several tables to be joined. Ranking: how to rank multiple answers e.g., “Return Woody Allen movies". #UnifiedDataAnalytics #SparkAISummit 18

19.Ask a query What movies have the same director as the movie “Revolutionary Road” #UnifiedDataAnalytics #SparkAISummit 19

20.Understanding Syntax NLQ Syntactic Parser Step 1. Understand the natural language query linguistically. Generate a dependency parse tree: • part-of-speech (POS) tags that describe each word's syntactic function + • syntactic relationships between words in the sentence. #UnifiedDataAnalytics #SparkAISummit 20

21.Understanding Syntax NLQ Syntactic Parser Step 2. 1. Map query elements to data elements: Node Mapper • tables, attributes, values – using indexes • commands (e.g., order by) – using a dictionary 2. Keep best mappings #UnifiedDataAnalytics #SparkAISummit 21

22.Understanding Syntax NLQ Syntactic Parser Node Mapper Step 3. Map the parse tree to the database structure and build a query tree Tree Mapper #UnifiedDataAnalytics #SparkAISummit 22

23.Understanding Syntax NLQ Syntactic Parser Node Mapper Tree Mapper SQL Generator Step 4. Generate the SQL query to execute SQL #UnifiedDataAnalytics #SparkAISummit 23

24.What movies have the same director as the movie “Revolutionary Road” ROOT Return director movies Syntactic Parser Node Mapper movie same “Revolutionary Road” Keyword Schema Element ROOT movie Movie “Revolutionary Movie.Tittle Return Same Tree Mapper Road” movies director director movies Movie movies movie director Director “Revolutionary Road” Main Query Block0 SELECT director.did, movie.mid SELECT DISTINCT movie.tittle FROM movie, director, directed_by FROM movie, block0, block1 SQL Generator WHERE movie.mid = block0.mid AND WHERE movie.mid = directed_by.msid directed_by.did = director.did AND block0.pk_director = block1.pk_director Block1 SELECT director.did, movie.mid FROM movie, director, directed_by WHERE movie.tittle = “Revolutionary Road” AND 24 #UnifiedAnalytics #SparkAISummit movie.mid = directed_by.msid AND

25.Understanding Syntax Why is Parsing So Hard For Computers to Get Right? • Human languages show remarkable levels of ambiguity. • It is not uncommon for moderate length sentences to have hundreds, thousands, or even tens of thousands of possible syntactic structures. • A natural language parser must somehow search through all of these alternatives, and find the most plausible structure given the context. #UnifiedDataAnalytics #SparkAISummit 25

26. Step 1: Let the user ask Syntax-based (NLP) using natural language Query Understanding Data-based Query Understanding NL Query Explanations Query Recommendations

27.Ask a query Show me Italian restaurants Not much value a parser can add #UnifiedDataAnalytics #SparkAISummit 27

28.Ambiguity Several possible mappings #UnifiedDataAnalytics #SparkAISummit 28

29. Ambiguity Likely Several possible query interpretations 1 "business categorized as restaurant and as Italian” “restaurant''= Category(category), "italian''= Category(category) 2 "business categorized as Italian whose name includes restaurant” Unlikely "restaurant''= Business(name), "italian''= Category(category) 3 "business categorized as Italian whose address includes restaurant” "restaurant''= Business(address), "italian''= Category(category) 4 "business categorized as restaurant that serves Italian” "restaurant''= Category(category), "italian''= Attribute(value) 5 "business whose address contains restaurant and Italian” "restaurant''= Business(address), "italian''= Business(address) #UnifiedDataAnalytics #SparkAISummit 29