scholarly journals Linguistic Summaries in Evaluating Elementary Conditions, Summarizing Data and Managing Nested Queries

Informatica ◽  
2020 ◽  
pp. 1-16
Author(s):  
Pavol Sojka ◽  
Miroslav Hudec ◽  
Miloš Švaňa
Keyword(s):  
2021 ◽  
Vol 1 (2) ◽  
pp. 340-364
Author(s):  
Rui Araújo ◽  
António Pinto

Along with the use of cloud-based services, infrastructure, and storage, the use of application logs in business critical applications is a standard practice. Application logs must be stored in an accessible manner in order to be used whenever needed. The debugging of these applications is a common situation where such access is required. Frequently, part of the information contained in logs records is sensitive. In this paper, we evaluate the possibility of storing critical logs in a remote storage while maintaining its confidentiality and server-side search capabilities. To the best of our knowledge, the designed search algorithm is the first to support full Boolean searches combined with field searching and nested queries. We demonstrate its feasibility and timely operation with a prototype implementation that never requires access, by the storage provider, to plain text information. Our solution was able to perform search and decryption operations at a rate of, approximately, 0.05 ms per line. A comparison with the related work allows us to demonstrate its feasibility and conclude that our solution is also the fastest one in indexing operations, the most frequent operations performed.


2013 ◽  
Vol 10 (1) ◽  
pp. 79-104
Author(s):  
Guillem Rull ◽  
Carles Farré ◽  
Ernest Teniente ◽  
Toni Urpí

With the emergence of the Web and the wide use of XML for representing data, the ability to map not only flat relational but also nested data has become crucial. The design of schema mappings is a semi-automatic process. A human designer is needed to guide the process, choose among mapping candidates, and successively refine the mapping. The designer needs a way to figure out whether the mapping is what was intended. Our approach to mapping validation allows the designer to check whether the mapping satisfies certain desirable properties. In this paper, we focus on the validation of mappings between nested relational schemas, in which the mapping assertions are either inclusions or equalities of nested queries. We focus on the nested relational setting since most XML?s Document Type Definitions (DTDs) can be represented in this model. We perform the validation by reasoning on the schemas and mapping definition. We take into account the integrity constraints defined on both the source and target schema. We consider constraints and mapping?s queries which may contain arithmetic comparisons and negations. This class of mapping scenarios is significantly more expressive than the ones addressed by previous work on nested relational mapping validation. We encode the given mapping scenario into a single flat database schema, so we can take advantage of our previous work on validating flat relational mappings, and reformulate each desirable property check as a query satisfiability problem.


Author(s):  
Sophie Cluet ◽  
Guido Moerkotte
Keyword(s):  

Author(s):  
Mathew George, Et. al.

Different methods and systems were proposed in the past for translating Natural Language (NL) statements in to Structured Query Language (SQL) queries. Translating statements resultingin‘nested’queries havealways been a challenge and was not effectively handled. This work proposes a framework for translating requirement statementsresulting inthe construction of nested Queries. While translating nested scenarios; often thereis a need to create sub-queriesthat execute inpipeline orin parallel or both operating together.Lambda Calculus is found to be effective in representing the intermediate expressions and helps in performing the transformations that are needed in translating specific predicates into SQL, but its inflexibility in combining parallel computations is a constraint. To represent clauses that are in parallel or arein pipeline,and to perform the required transformationson theintermediate expressions involving these,more advancedprogramming constructs are needed.This work recommends the use of advanced language constructs and adoptsfunctional programming techniques for performing the required transformation at the intermediate language level.


2020 ◽  
Author(s):  
Aristotelis Leventidis ◽  
Jiahui Zhang ◽  
Cody Dunne ◽  
Wolfgang Gatterbauer ◽  
H.V. Jagadish ◽  
...  

Understanding the meaning of existing SQL queries is critical for code maintenance and reuse. Yet SQL can be hard to read, even for expert users or the original creator of a query. We conjecture that it is possible to capture the logical intent of queries in automatically-generated visual diagrams that can help users understand the meaning of queries faster and more accurately than SQL text alone.We present initial steps in that direction with visual diagrams that are based on the first-order logic foundation of SQL and can capture the meaning of deeply nested queries. Our diagrams build upon a rich history of diagrammatic reasoning systems in logic and were designed using a large body of human-computer interaction best practices: they are minimal in that no visual element is superfluous; they are unambiguous in that no two queries with different semantics map to the same visualization; and they extend previously existing visual representations of relational schemata and conjunctive queries in a natural way. An experimental evaluation involving 42 users on Amazon Mechanical Turk shows that with only a 2--3 minute static tutorial, participants could interpret queries meaningfully faster with our diagrams than when reading SQL alone. Moreover, we have evidence that our visual diagrams result in participants making fewer errors than with SQL. We believe that more regular exposure to diagrammatic representations of SQL can give rise to a pattern-based and thus more intuitive use and re-use of SQL.A full version of this paper with all appendices and supplemental material for the experimental study (stimuli, raw data, and analysis code) are available at https://osf.io/btszh


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