Reasoning about “reasoning about reasoning”: semantics and contextual equivalence for probabilistic programs with nested queries and recursion

Metareasoning can be achieved in probabilistic programming languages (PPLs) using agent models that recursively nest inference queries inside inference queries. However, the semantics of this powerful, reflection-like language feature has defied an operational treatment, much less reasoning principles for contextual equivalence. We give formal semantics to a core PPL with continuous distributions, scoring, general recursion, and nested queries. Unlike prior work, the presence of nested queries and general recursion makes it impossible to stratify the definition of a sampling-based operational semantics and that of a measure-theoretic semantics—the two semantics must be defined mutually recursively. A key yet challenging property we establish is that probabilistic programs have well-defined meanings: limits exist for the step-indexed measures they induce. Beyond a semantics, we offer relational reasoning principles for probabilistic programs making nested queries. We construct a step-indexed, biorthogonal logical-relations model. A soundness theorem establishes that logical relatedness implies contextual equivalence. We demonstrate the usefulness of the reasoning principles by proving novel equivalences of practical relevance—in particular, game-playing and decisionmaking agents. We mechanize our technical developments leading to the soundness proof using the Coq proof assistant. Nested queries are an important yet theoretically underdeveloped linguistic feature in PPLs; we are first to give them semantics in the presence of general recursion and to provide them with sound reasoning principles for contextual equivalence.

Secure Remote Storage of Logs with Search Capabilities

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.

A novel framework for synthesizing nested queries in SQL from business requirements language

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.

QueryVis: Logic-based diagrams help users understand complicated SQL queries faster

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

Penerapan metode nested query untuk menetukan siswa penerima bantuan PIP

Cijujung 04 State Elementary School is one of the basic educational institutions that is always developing with the number of students increasing from time to time. Cijujung 04 Public Elementary School is a state school, this school is located in the Cikupa-Nagrak village of Cijujung village, cibungbulang sub-district, Bogor Regency. Public elementary schools or other private elementary schools also have very important data. One of them is a master book. The student handbook is a data set of students studying at the school. Student Parent Book is one of the most important parts of the management of this institution. The notes in the master book must be complete. Includes, student biodata, student grades, and student achievement results from the first semester to graduate school. However, in its implementation so far it has been ineffective and inefficient because it is carried out manually, namely by writing data in a thick ledger or a master book. The teacher also had difficulty finding student data in the parent book and also sought students for prospective recipients of PIP assistance. A system is needed that can be used by teachers to find student data in the parent book and also look for students for potential recipients of PIP assistance. Nested queries or queries are queries that have other queries in them. By using the nested query method in the parent book system, the search process for prospective PIP recipient students can be searched as needed because in the parent book system has a student search feature based on the desired category. Based on the nested query method. From the results of the distribution of questionnaires to test the system distributed to informatics engineering system respondents obtained a percentage of system eligibility of 87% so that it can be categorized as very feasible, while the results of distributing questionnaires to user respondents obtained a percentage of system feasibility of 94.5% so that it can be categorized as very feasible

An algebra for distributed Big Data analytics

We present an algebra for data-intensive scalable computing based on monoid homomorphisms that consists of a small set of operations that capture most features supported by current domain-specific languages for data-centric distributed computing. This algebra is being used as the formal basis of MRQL, which is a query processing and optimization system for large-scale distributed data analysis. The MRQL semantics is given in terms of monoid comprehensions, which support group-by and order-by syntax and can work on heterogeneous collections without requiring any extension to the monoid algebra. We present the syntax and semantics of monoid comprehensions and provide rules to translate them to the monoid algebra. We give evidence of the effectiveness of our algebra by presenting some important optimization rules, such as converting nested queries to joins.

Constructing Well-Organized Wireless Sensor Networks with Low-level Identification

In most distributed systems, naming of nodes for low-level communication leverages topological location (such as node addresses) and is independent of any application. In this paper, we investigate an emerging class of distributed systems where low-level communication does not rely on network topological location. Rather, low-level communication is based on attributes that are external to the network topology and relevant to the application. When combined with dense deployment of nodes, this kind of named data enables in-network processing for data aggregation, collaborative signal processing, and similar problems. These approaches are essential for emerging applications such as sensor networks where resources such as bandwidth and energy are limited. This paper is the first description of the software architecture that supports named data and in-network processing in an operational, multi-application sensor-network. We show that approaches such as in-network aggregation and nested queries can significantly affect network traffic. In one experiment aggregation reduces traffic by up to 42% and nested queries reduce loss rates by 30%. Although aggregation has been previously studied in simulation, this paper demonstrates nested queries as another form of in-network processing, and it presents the first evaluation of these approaches over an operational test bed.