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Verifying Text Summaries of Relational Data Sets

Relational data is often summarized by text
• Focus of this paper is problem of verifying, in an automated fashion, Whether text claims are consistent with the actual database.
• The authors proposed a tool for verifying text summaries of relational Data sets, which works similar to spell checker and marks up claims That are believed to be erroneous. 
• System converts claims into SQL queries then evaluates them. 
• Main problem is converting natural language claims to SQL query. • Tool is called AggChecker.
--• AggChecker consists of two parts: a Relational data set and a text document. • The text contains claims about the data. • Goal is to translate natural language claims Into pairs of SQL queries and claimed query Results 
Keyword Matching:::Each claim in the input text can be associated with relevant keywords-Extracting Keywords from Text--Constructing Likely Query Candidates.
Probabilistic Model and Query Evaluation.
 • Introduced the problem of fact-checking natural language summaries Of relational databases
• Presented a first corresponding approach, encapsulated into a novel Tool called the AggChecker 
• Successfully used it to identify erroneous claims in articles from major Newspapers.
AStream: Ad-hoc Shared Stream Processing
Ad-hoc Stream Requirements: 1. Integration 2. Consistency 3. Performance
OPTIMIZATION:1. Incremental query processing2. Data copy and shuffling3. Memory efficient dynamic slice data structure.
Exactly-Once SEMANTICS::
 Every input tuple is only processed once, even under failures. Astream is deterministic because all its distributed components are Deterministic. Event-time semantics ensure correctness on out-of-order events or During replays of data.
RELATED WORK:::: Query-at-a-Time Processing-- Stream Multi-Query Optimization--- Adaptive Query Optimization ---Batch ad-hoc Query Processing Systems--- Streaming query sharing
FUTURE WORD::::: In future work, we plan to extend AStream with a cost Based optimizer and adaptive query processing techniques. Based on Sharing statistics among queries collected at runtime, a more optimal Query plan can be generated by grouping similar queries.
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning
• Uses deep RL to learn and recommend confgurations for databases 
• Uses only limited number of samples • Designed to work end-to-end • It has good adaptability to environment changes • Signifcantly outperformed the state-of-the-art tuning tools and DBA Experts .
**Reinforcement Learning is a general-purpose Framework for decision-making. It basically learns by trial and error.
TRAINING DATA GENERATION::::• Cold start • Incremental Training:
• The process starts with the offline training 
• The training data is a set of training quadruples ⟨q, a, s, r⟩ 
  -- q: a set of query workloads (i.E. SQL queries) 
  -- a: a set of knobs as well as their values when processing q 
  --s: the database state (which is a set of 63 metrics) when processing q 
  -- r: the performance when processing q (including throughput and Latency).
 • An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning is proposed
 • Its superiority is demonstrated with the extensive experimental results
 • It is much faster than DBAs and other methods
 • It has a good adaptability against workload and environment changes

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