Query Fragments Research Articles

Languages Generated by Conjunctive Query Fragments of FC[REG

Abstract$${\textsf{FC}}$$ FC is a finite model variant on the theory of concatenation, $${\textsf{FC}[\textsf{REG}]}$$ FC [ REG ] extends $${\textsf{FC}}$$ FC with regular constraints. This paper considers the languages generated by their conjunctive query fragments, and . We compare the expressive power of $${\textsf {FC[REG]-CQ}}$$ FC [ REG ] - CQ to that of various related language generators, such as regular expressions, patterns, and typed patterns. We then consider decision problems for $${\textsf {FC-CQ}}$$ FC - CQ and $${\textsf {FC[REG]-CQ}}$$ FC [ REG ] - CQ , and show that certain static analysis problems (such as equivalence and regularity) are undecidable. While this paper defines $${\textsf {FC-CQ}}$$ FC - CQ based on the logic $${\textsf{FC}}$$ FC , it can equally be understood as synchronized intersections of pattern languages, or as systems of restricted word equations.

PoreMatMod.jl: Julia Package for in Silico Postsynthetic Modification of Crystal Structure Models.

PoreMatMod.jl is a free, open-source, user-friendly, and documented Julia package for modifying crystal structure models of porous materials such as metal-organic frameworks (MOFs). PoreMatMod.jl functions as a find-and-replace algorithm on crystal structures by leveraging (i) Ullmann's algorithm to search for subgraphs of the crystal structure graph that are isomorphic to the graph of a query fragment and (ii) the orthogonal Procrustes algorithm to align a replacement fragment with a targeted substructure of the crystal structure for installation. The prominent application of PoreMatMod.jl is to generate libraries of hypothetical structures for virtual screenings. For example, one can install functional groups on the linkers of a parent MOF, mimicking postsynthetic modification. Other applications of PoreMatMod.jl to modify crystal structure models include introducing defects with precision and correcting artifacts of X-ray structure determination (adding missing hydrogen atoms, resolving disorder, and removing guest molecules). The find-and-replace operations implemented by PoreMatMod.jl can be applied broadly to diverse atomistic systems for various in silico structural modification tasks.

Columns Occurrences Graph to Improve Column Prediction in Deep Learning Nlidb

Natural language interfaces to databases (NLIDB) has been a research topic for a decade. Significant data collections are available in the form of databases. To utilize them for research purposes, a system that can translate a natural language query into a structured one can make a huge difference. Efforts toward such systems have been made with pipelining methods for more than a decade. Natural language processing techniques integrated with data science methods are researched as pipelining NLIDB systems. With significant advancements in machine learning and natural language processing, NLIDB with deep learning has emerged as a new research trend in this area. Deep learning has shown potential for rapid growth and improvement in text-to-SQL tasks. In deep learning NLIDB, closing the semantic gap in predicting users’ intended columns has arisen as one of the critical and fundamental problems in this research field. Contributions toward this issue have consisted of preprocessed feature inputs and encoding schema elements afore of and more impactful to the targeted model. Various significant work contributed towards this problem notwithstanding, this has been shown to be one of the critical issues for the task of developing NLIDB. Working towards closing the semantic gap between user intention and predicted columns, we present an approach for deep learning text-to-SQL tasks that includes previous columns’ occurrences scores as an additional input feature. Overall exact match accuracy can also be improved by emphasizing the improvement of columns’ prediction accuracy, which depends significantly on column prediction itself. For this purpose, we extract the query fragments from previous queries’ data and obtain the columns’ occurrences and co-occurrences scores. Column occurrences and co-occurrences scores are processed as input features for the encoder–decoder-based text to the SQL model. These scores contribute, as a factor, the probability of having already used columns and tables together in the query history. We experimented with our approach on the currently popular text-to-SQL dataset Spider. Spider is a complex data set containing multiple databases. This dataset includes query–question pairs along with schema information. We compared our exact match accuracy performance with a base model using their test and training data splits. It outperformed the base model’s accuracy, and accuracy was further boosted in experiments with the pretrained language model BERT.

The evolution of Amazon redshift

The evolution of Amazon redshift

When Are Search Completion Suggestions Problematic?

Problematic web search query completion suggestions-perceived as biased, offensive, or in some other way harmful-can reinforce existing stereotypes and misbeliefs, and even nudge users towards undesirable patterns of behavior. Locating such suggestions is difficult, not only due to the long-tailed nature of web search, but also due to differences in how people assess potential harms. Grounding our study in web search query logs, we explore when system-provided suggestions might be perceived as problematic through a series of crowd-experiments where we systematically manipulate: the search query fragments provided by users, possible user search intents, and the list of query completion suggestions. To examine why query suggestions might be perceived as problematic, we contrast them to an inventory of known types of problematic suggestions. We report our observations around differences in the prevalence of a) suggestions that are problematic on their own versus b) suggestions that are problematic for the query fragment provided by a user, for both common informational needs and in the presence of web search voids-topics searched by few to no users. Our experiments surface a rich array of scenarios where suggestions are considered problematic, including due to the context in which they were surfaced. Compounded by the elusive nature of many such scenarios, the prevalence of suggestions perceived as problematic only for certain user inputs, raises concerns about blind spots due to data annotation practices that may lead to some types of problematic suggestions being overlooked.

Scrutinizer

We demonstrate Scrutinizer, a system that supports human fact checkers in translating text claims into SQL queries on an associated database. Scrutinizer coordinates teams of human fact checkers and reduces their verification time by proposing queries or query fragments over relevant data. Those proposals are based on claim text classifiers, that gradually improve during the verification of multiple claims. In addition, Scrutinizer uses tentative execution of query candidates to narrow down the set of alternatives. The verification process is controlled by a cost-based optimizer that plans effective question sequences to verify specific claims, and prioritizes claims for verification. In this demonstration, we first show how our system can assist users in verifying statistical claims. We then let users come up with new, unseen claims and show how the system effectively learns new queries with little user feedback.

Scrutinizer

Organizations spend significant amounts of time and money to manually fact check text documents summarizing data. The goal of the Scrutinizer system is to reduce verification overheads by supporting human fact checkers in translating text claims into SQL queries on an database. Scrutinizer coordinates teams of human fact checkers. It reduces verification time by proposing queries or query fragments to the users. Those proposals are based on claim text classifiers, that gradually improve during the verification of a large document. In addition, Scrutinizer uses tentative execution of query candidates to narrow down the set of alternatives. The verification process is controlled by a cost-based optimizer. It optimizes the interaction with users and prioritizes claim verifications. For the latter, it considers expected verification overheads as well as the expected claim utility as training samples for the classifiers. We evaluate the Scrutinizer system using simulations and a user study with professional fact checkers, based on actual claims and data. Our experiments consistently demonstrate significant savings in verification time, without reducing result accuracy.

On the Expressivity of ASK Queries in SPARQL

As a major query type in SPARQL, ASK queries are boolean queries and have found applications in several domains such as semantic SPARQL optimization. This paper is a first systematic study of the relative expressive power of various fragments of ASK queries in SPARQL. Among many new results, a surprising one is that the operator UNION is redundant for ASK queries. The results in this paper as a whole paint a rich picture for the expressivity of fragments of ASK queries with the four basic operators of SPARQL 1.0 possibly together with a negation. The work in this paper provides a guideline for future SPARQL query optimization and implementation.

Fragger: a protein fragment picker for structural queries.

Protein modeling and design activities often require querying the Protein Data Bank (PDB) with a structural fragment, possibly containing gaps. For some applications, it is preferable to work on a specific subset of the PDB or with unpublished structures. These requirements, along with specific user needs, motivated the creation of a new software to manage and query 3D protein fragments. Fragger is a protein fragment picker that allows protein fragment databases to be created and queried. All fragment lengths are supported and any set of PDB files can be used to create a database. Fragger can efficiently search a fragment database with a query fragment and a distance threshold. Matching fragments are ranked by distance to the query. The query fragment can have structural gaps and the allowed amino acid sequences matching a query can be constrained via a regular expression of one-letter amino acid codes. Fragger also incorporates a tool to compute the backbone RMSD of one versus many fragments in high throughput. Fragger should be useful for protein design, loop grafting and related structural bioinformatics tasks.

Fragger: a protein fragment picker for structural queries

Protein modeling and design activities often require querying the Protein Data Bank (PDB) with a structural fragment, possibly containing gaps. For some applications, it is preferable to work on a specific subset of the PDB or with unpublished structures. These requirements, along with specific user needs, motivated the creation of a new software to manage and query 3D protein fragments. Fragger is a protein fragment picker that allows protein fragment databases to be created and queried. All fragment lengths are supported and any set of PDB files can be used to create a database. Fragger can efficiently search a fragment database with a query fragment and a distance threshold. Matching fragments are ranked by distance to the query. The query fragment can have structural gaps and the allowed amino acid sequences matching a query can be constrained via a regular expression of one-letter amino acid codes. Fragger also incorporates a tool to compute the backbone RMSD of one versus many fragments in high throughput. Fragger should be useful for protein design, loop grafting and related structural bioinformatics tasks.

Fragger: a protein fragment picker for structural queries.

Protein modeling and design activities often require querying the Protein Data Bank (PDB) with a structural fragment, possibly containing gaps. For some applications, it is preferable to work on a specific subset of the PDB or with unpublished structures. These requirements, along with specific user needs, motivated the creation of a new software to manage and query 3D protein fragments. Fragger is a protein fragment picker that allows protein fragment databases to be created and queried. All fragment lengths are supported and any set of PDB files can be used to create a database. Fragger can efficiently search a fragment database with a query fragment and a distance threshold. Matching fragments are ranked by distance to the query. The query fragment can have structural gaps and the allowed amino acid sequences matching a query can be constrained via a regular expression of one-letter amino acid codes. Fragger also incorporates a tool to compute the backbone RMSD of one versus many fragments in high throughput. Fragger should be useful for protein design, loop grafting and related structural bioinformatics tasks.

Capability-based localization of distributed and heterogeneous queries

AbstractOne key aspect of data-centric applications is the manipulation of data stored in persistent repositories, which is moving fast from querying a centralized relational database to the ad-hoc combination of constellations of data sources. The extension of general purpose languages with query operations is increasingly popular, as a tool to improve reasoning and optimizing capabilities of interpreters and compilers. However, not much is being done to integrate and orchestrate different and separate sources of data. We present a data manipulation language that abstracts the nature and location of data-sources. We define its semantics and a type directed query localization mechanism to be used in development tools for heterogeneous environments to efficiently compile them into native queries. We introduce a localization procedure based on rewriting of query expressions that is confluent, terminating and provides the maximum mapping between site capabilities and the structure of the query. We provide formal type safety results that support the sound distribution of query fragments over remote sites. Our approach is also suitable for an interactive query construction environment by rich user interfaces that provide immediate feedback on data manipulation operations. This approach is currently the base for the data layer of a development platform for mobile and web applications.

$$\mathcal {IRORS}$$ IRORS : intelligent recommendation of RSS feeds

The abundance of information prohibits getting relevant results on online social researches. Thus, RSS feeds appear as monitoring tool of current events according to users preferences. However, the user is flooded by the amount of such RSS feeds. For that reason, any analysis of RSS feeds seems effortful and complex. In this paper, we aim to improve the effectiveness and swiftness of pertinent RSS feeds analysis through recommending suitable fragments of queries during the analysis process of events. Accordingly, we propose an innovative architecture of our new active RSS feeds warehouse. Additionally, we introduce a new recommender system to improve the querying expression of RSS feeds. Our experiment results show the robustness and efficiency of our approach.

A Hybrid Approach to Answering Why-Not Questions on Relational Query Results

In analyzing and debugging data transformations, or more specifically relational queries, a subproblem is to understand why some data are not part of the query result. This problem has recently been addressed from different perspectives for various fragments of relational queries. The different perspectives yield different yet complementary explanations of such missing answers . This article first aims at unifying the different approaches by defining a new type of explanation, called hybrid explanation, that encompasses the variety of previously defined types of explanations. This solution goes beyond simply forming the union of explanations produced by different algorithms and is shown to be able to explain a larger set of missing answers. Second, we present Conseil , an algorithm to generate hybrid explanations. Conseil is also the first algorithm to handle nonmonotonic queries. Experiments on efficiency and explanation quality show that Conseil is comparable and even outperforms previous algorithms. This article extends a previous short conference paper by providing proofs, additional theorems, and a detailed discussion of each step of the Conseil algorithm. It also significantly extends the experimental evaluation on efficiency and explanation quality.

QueRIE: Collaborative Database Exploration

Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the user's querying behavior and finds matching patterns in the system's query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these “similar” users and their queries to recommend queries that the current user may find interesting. In this work we describe an instantiation of the QueRIE framework, where the active user's session is represented by a set of query fragments. The recorded fragments are used to identify similar query fragments in the previously recorded sessions, which are in turn assembled in potentially interesting queries for the active user. We show through experimentation that the proposed method generates meaningful recommendations on real-life traces from the SkyServer database and propose a scalable design that enables the incremental update of similarities, making real-time computations on large amounts of data feasible. Finally, we compare this fragment-based instantiation with our previously proposed tuple-based instantiation discussing the advantages and disadvantages of each approach.

In Silico Bioisosteric Replacements of hnRNP K Ligands as Anticancer Lead Compounds

Heterogeneous ribonucleoprotein K (hnRNP K) is a constitutive protein found in nucleus, cytoplasm and mitochondria of cells and interacts with diverse molecules involved in gene expression and signal transduction. Its over expression is associated with the development of prostate, breast and colorectal cancer types. The binding to nucleotides is the main interaction that triggers biological activity and is mediated by its three K homology (KH) domains. Trying to optimize a benzimidazole and a phenylbenzamide derivatives, already reported as hnRNP K ligands, and generate novel ligand candidates with potential anticancer activity, bioisosteric replacements were suggested in the molecular groups able to perform polar interactions with R40 and R59, the main residues of KH3 domain responsible for nucleotide recognition. The top-ranked fragments from BROOD database regarding interaction with the protein, and also steric and electrostatic similarity with query fragments, were selected as potential bioisosters. The novel fragments when inserted in the benzimidazole and phenyilbenzamide derivatives could interact with R40 or R59 by hydrogen bond or ionic interaction. Some of the selected fragments show toxicophoric groups able to induce hepatotoxicity, carcinogenicity and chromosome damage. In this way, the bioisosters without classical toxicophoric groups should be prioritized for synthesis of novel lead compounds, generating diversity in the continuous search of effective and safer anticancer drugs.

A Survey Report on the Novel Approach on Use of Recommendation Outline in Query Recommender System

The DBMS applications are becoming increasingly popular in the scientific community to support the interactive exploration of huge data. A result of heavy usage has also lead to a lot of tavles generated in the warehouse. This has tremendously increased the need for recommendation system & various tools for the user as user is not capable to explore such huge data by using Structured Query language. This motivates to turn towards the idea of query recommendation system which will assist the technical & non-technical users to generate top-N query recommendations as per their needs. In this survey paper, the focus is given on opportunities that exist in domain of query recommendation, brief description of recommendation outline and techniques. Along with this, the terminology to identify the similarity and comparison of fragment based approach & matrix factorization approach is also explored. The Database Management Systems are used to organize, maintain and retrieve the huge amount of data. To acquire the essential data the user need to interact with the database by taking the support of declarative Structured Query Language (SQL). Most of times, it is observed that user faces the difficulties in interacting with the database by using SQL query language as the database have hundreds of schema's and thousands of attributes. This problem has been solved with the support of Database Management Applications such as SAP, Easy Query, and Microsoft Access. These applications involve the user interaction and manual editing of the SQL query components but when the user is unacquainted about the underlying database structure then it becomes difficult for the user to formulate the query. To formulate the query, the research work has been done (1) and that results into the query assistant tool which helps the user to either formulate the query or to recommend the relevant query items based on formulated query. This work is the reflection of the web recommendation techniques those are used in gaining the on-line marketing intelligence. The web recommendation techniques and algorithms have their strong relation with the Information Retrieval Science (IR). In IR, the recommendation techniques and algorithms are used to retrieve the most relevant top K documents as per the formulated query. Most of times the Business Verticals have their own off-line database and the user will be interested in exploration of the database and faces the difficulties. Therefore, it may also possible to extend the idea specified by the authors (1) to provide the Query Recommendation Assistant Tool that enable the user to formulate and refine the SQL query. This take off to cataloged view on how to make the imperative use of IR and the web recommendation techniques to generate and recommend the top K relevant query objects by identifying the similarities among the SQL query fragments. This becomes the novel approach that handles crucial responsibility in proposing the assistant tool to retrieve data over the database. The rest of the paper is organized as follows: section-2 describes the related work and the section-3 concentrates on recommendation outline. The section-4 describes the recommendation techniques and similarity metrics those have been used in finding the similarity among the query fragments. The section-5 focuses on terminology used to calculate weights of items required for finding similarity. The section-6 states the comparison between the fragment based approach and matrix factorization approach.

Query reverse engineering

In this paper, we introduce a new problem termed query reverse engineering (QRE). Given a database $$D$$ D and a result table $$T$$ T --the output of some known or unknown query $$Q$$ Q on $$D$$ D --the goal of QRE is to reverse-engineer a query $$Q'$$ Q ? such that the output of query $$Q'$$ Q ? on database $$D$$ D (denoted by $$Q'(D)$$ Q ? ( D ) ) is equal to $$T$$ T (i.e., $$Q(D)$$ Q ( D ) ). The QRE problem has useful applications in database usability, data analysis, and data security. In this work, we propose a data-driven approach, TALOS for Tree-based classifier with At Least One Semantics, that is based on a novel dynamic data classification formulation and extend the approach to efficiently support the three key dimensions of the QRE problem: whether the input query is known/unknown, supporting different query fragments, and supporting multiple database versions.

Fragment-based Shape Signatures: a new tool for virtual screening and drug discovery

Since its introduction in 2003, the Shape Signatures method has been successfully applied in a number of drug design projects. Because it uses a ray-tracing approach to directly measure molecular shape and properties (as opposed to relying on chemical structure), it excels at scaffold hopping, and is extraordinarily easy to use. Despite its advantages, a significant drawback of the method has hampered its application to certain classes of problems; namely, when the chemical structures considered are large and contain heterogeneous ring-systems, the method produces descriptors that tend to merely measure the overall size of the molecule, and begin to lose selective power. To remedy this, the approach has been reformulated to automatically decompose compounds into fragments using ring systems as anchors, and to likewise partition the ray-trace in accordance with the fragment assignments. Subsequently, descriptors are generated that are fragment-based, and query and target molecules are compared by mapping query fragments onto target fragments in all ways consistent with the underlying chemical connectivity. This has proven to greatly extend the selective power of the method, while maintaining the ease of use and scaffold-hopping capabilities that characterized the original implementation. In this work, we provide a full conceptual description of the next generation Shape Signatures, and we underline the advantages of the method by discussing its practical applications to ligand-based virtual screening. The new approach can also be applied in receptor-based mode, where protein-binding sites (partitioned into subsites) can be matched against the new fragment-based Shape Signatures descriptors of library compounds.Electronic supplementary materialThe online version of this article (doi:10.1007/s10822-013-9698-7) contains supplementary material, which is available to authorized users.

Fast in‐memory XPath search using compressed indexes

SummaryExtensible Markup Language (XML) documents consist of text data plus structured data (markup). XPath allows to query both text and structure. Evaluating such hybrid queries is challenging. We present a system for in‐memory evaluation of XPath search queries, that is, queries with text and structure predicates, yet without advanced features such as backward axes, arithmetics, and joins. We show that for this query fragment, which contains Forward Core XPath, our system, dubbed Succinct XML Self‐Index (‘SXSI’), outperforms existing systems by 1–3 orders of magnitude. SXSI is based on state‐of‐the‐art indexes for text and structure data. It combines two novelties. On one hand, it represents the XML data in a compact indexed form, which allows it to handle larger collections in main memory while supporting powerful search and navigation operations over the text and the structure. On the other hand, it features an execution engine that uses tree automata and cleverly chooses evaluation orders that leverage the speeds of the respective indexes. SXSI is modular and allows seamless replacement of its indexes. This is demonstrated through experiments with (1) a text index specialized for search of bio sequences, and (2) a word‐based text index specialized for natural language search. Copyright © 2013 John Wiley & Sons, Ltd.