Query Representation Research Articles

Modelling Biodiversity Linked Data: Pragmatism May Narrow Future Opportunities

As the biodiversity community increasingly adopts Semantic Web (SW) standards to represent taxonomic registers, trait banks or museum collections, some questions come up relentlessly: How to model the data? For what goals? Can the same model fulfill different goals?So far, the community has mostly considered the SW standards through their most salient manifestation: the Web of Linked Data (Heath and Bizer 2011). Indeed, the 5-star Linked Data principles are geared towards the building of a large, distributed knowledge graph that may successfully fulfill biodiversity’s need for interoperability and data integration. However, the SW addresses a much broader set of problems involving automatic reasoning. For instance, reasoners can exploit ontological knowledge to improve query answering, leverage class definitions to infer class subsumption relationships, or classify individualsi.e.compute instance relationships between individuals and classes by applying reasoning techniques on class definitions and instance descriptions (Shearer et al. 2008).Whether a "thing" should be modelled as a class or a class instance has been debated at length in the SW community, and the answer is often a matter of perspective. In the context of taxonomic registers for example, the NCBI Organismal Classification (Federhen 2012) and Vertebrate Taxonomy Ontology (Midford et al. 2013) represent taxa as classes in the Ontology Web Language (OWL). By contrast, other initiatives represent taxa as instances of various classes,e.g.the SKOS Concept class (skos:Concept) in the AGROVOC thesaurus (Caracciolo et al. 2013) (we speak of the instances asSKOS concepts), the Darwin Core taxon class (dwc:Taxon)in Encyclopedia of Life (Parr et al. 2016), or classes depicting taxonomic ranks in GeoSpecies, DBpedia and the BBC Wildlife Ontology. Such modelling discrepancies impede linking congruent taxa throughout taxonomic registers. Indeed, one can state the equivalence between two classes (withowl:equivalentClass) or two class instances (withowl:sameAs, skos:exactMatch, etc.), but good practices discourage the alignment of classes with class instances (Baader et al. 2003).Recently, Darwin Core's popularity has fostered the modeling of taxa as instances of classdwc:Taxon(Senderov et al. 2018, Parr et al. 2016). In this context, pragmatism may incline a Linked Data provider to comply with this majority trend to ensure maximum interlinking. Although technically and conceptually valid, this choice entails certain drawbacks. First, considering a taxon only as a an instance misses the fact that it is a set of biological individuals with common characteristics. An OWL class exactly captures this semantics through the set of necessary and sufficient conditions that an individual must meet to be a class member. In turn, an OWL reasoner can leverage this knowledge to perform query answering, compute subsumption or instance relationships. By contrast, taxa depicted by class instances are notdefinedbutdescribedby stating their properties. Hence the second drawback: unless we develop bespoke reasoners, there is not much a standard OWL reasoner can deduce from instances.Yet, some works have demonstrated the effectiveness of logic representation and reasoning capabilities,e.g.computing the alignments of two primate classifications (Franz et al. 2016) using generic reasoners that nevertheless require proprietary input formats. OWL reasoners are typically designed to solve such classification problems. They may leverage taxonomic ontologies to compute alignments with other ontologies or apply reasoning to individuals' properties to infer their species. Hence, pragmatically following the instance-based approach may indeed maximize interlinking in the short term, but bears the risk of denying ourselves potentially desirable use cases in the longer term. We believe that developing class-based ontologies for biodiversity should help leverage the SW’s extensive theoretical and practical works to tackle a variety of use cases that so far have been addressed with bespoke solutions.

LAILAPS-QSM: A RESTful API and JAVA library for semantic query suggestions.

In order to access and filter content of life-science databases, full text search is a widely applied query interface. But its high flexibility and intuitiveness is paid for with potentially imprecise and incomplete query results. To reduce this drawback, query assistance systems suggest those combinations of keywords with the highest potential to match most of the relevant data records. Widespread approaches are syntactic query corrections that avoid misspelling and support expansion of words by suffixes and prefixes. Synonym expansion approaches apply thesauri, ontologies, and query logs. All need laborious curation and maintenance. Furthermore, access to query logs is in general restricted. Approaches that infer related queries by their query profile like research field, geographic location, co-authorship, affiliation etc. require user’s registration and its public accessibility that contradict privacy concerns. To overcome these drawbacks, we implemented LAILAPS-QSM, a machine learning approach that reconstruct possible linguistic contexts of a given keyword query. The context is referred from the text records that are stored in the databases that are going to be queried or extracted for a general purpose query suggestion from PubMed abstracts and UniProt data. The supplied tool suite enables the pre-processing of these text records and the further computation of customized distributed word vectors. The latter are used to suggest alternative keyword queries. An evaluated of the query suggestion quality was done for plant science use cases. Locally present experts enable a cost-efficient quality assessment in the categories trait, biological entity, taxonomy, affiliation, and metabolic function which has been performed using ontology term similarities. LAILAPS-QSM mean information content similarity for 15 representative queries is 0.70, whereas 34% have a score above 0.80. In comparison, the information content similarity for human expert made query suggestions is 0.90. The software is either available as tool set to build and train dedicated query suggestion services or as already trained general purpose RESTful web service. The service uses open interfaces to be seamless embeddable into database frontends. The JAVA implementation uses highly optimized data structures and streamlined code to provide fast and scalable response for web service calls. The source code of LAILAPS-QSM is available under GNU General Public License version 2 in Bitbucket GIT repository: https://bitbucket.org/ipk_bit_team/bioescorte-suggestion

Modeling Queries with Contextual Snippets for Information Retrieval

Query expansion under the pseudo-relevance feedback (PRF) framework has been extensively studied in information retrieval. However, most expansion methods are mainly based on the statistics of single terms, which can generate plenty of irrelevant query terms and decrease retrieval performance. To alleviate this problem, we propose an approach that adapts the PRF-based contextual snippets into a context-aware topic model to enhance query representations. Specifically, instead of selecting a series of independent terms, we make full use of the query contextual information and focus on the snippets with the length of n in the PRF documents. Furthermore, we propose a context-aware topic (CAT) model to mine the topic distributions of the query-relevant snippets, namely, fine contextual snippets. In contrast to the traditional topic models that infer the topics from the whole corpus, we establish a bridge between the snippets and the corresponding PRF documents, which can be used for modeling the topics more precisely and efficiently. Finally, the topic distributions of the fine snippets are used for context-aware and topic-sensitive query representations. To evaluate the performance of our approach, we integrate the obtained queries into a topic-based hybrid retrieval model and conduct extensive experiments on various TREC collections. The experimental results show that our query-modeling approach is more effective in boosting retrieval performance compared with the state-of-the-art methods.

Метод структурирования контента гетерогенного информационного пространства на основе формализованной модели предметной области для решения задач интеллектуального поиска

The problems of construction and integration of information retrieval systems with corporate information systems are considered. A model of information query and search results for the database and file data stores is described. The task of information retrieval is represented as the interaction of domains using an adapter. The task of the adapter is to convert the information needs, expressed in the language of a particular document into a search query, presented in the language of the information retrieval system. The representation of search queries in the language of the search engine assumes the following order of operations: the definition of the target entity, the conversion of the criteria specified by the user, and the addition of criteria specified by the source system. An algorithm for the order of the transformation is proposed in the general form. The implementation of the proposed model in the PHP framework of the web portal based on the Apache Solr search engine is considered.

A Novel Representation and Compression for Queries on Trajectories in Road Networks

Recording and querying time-stamped trajectories incurs high cost of data storage and computing. In this paper, we explore several characteristics of the trajectories in road networks, which have motivated the idea of coding trajectories by associating timestamps with relative spatial path and locations. Such a representation contains a large number of duplicate information to achieve a lower entropy compared with the existing representations, thereby drastically cutting the storage cost. We propose several techniques to compress spatial path and locations separately, which can support fast positioning and achieve better compression ratio. For locations, we propose two novel encoding schemes such that the binary code can preserve distance information, which is very helpful for LBS applications. In addition, an unresolved question in this area is whether it is possible to perform a search directly on the compressed trajectories, and if the answer is yes, then how. Here, we show that directly querying compressed trajectories based on our encoding scheme is possible and can be done efficiently. We design a set of primitive operations for this purpose, and propose index structures to reduce query response time. We demonstrate the advantage of our method and compare it against existing ones through a thorough experimental study on real trajectories in road network.

ISPEED: an Efficient In-Memory Based Spatial Query System for Large-Scale 3D Data with Complex Structures.

Recent advances in digital pathology make it possible to support 3D tissue-based investigation of human diseases at extremely high resolutions. Exploring spatial relationships and patterns among massive 3D micro-anatomic biological objects such as blood vessels and cells derived from 3D pathology image volumes plays a critical role in studying human diseases. In this paper, we present our work on building an effective and scalable in-memory based spatial query system iSPEED for large-scale 3D data with complex structures. To achieve low latency, iSPEED stores data in memory with effective progressive compression for each 3D object with successive levels of detail. To minimize search space and computation cost, iSPEED pregenerates global spatial indexes in memory and employs on-demand indexing at run-time. In particular, iSPEED exploits structural indexing for complex structured objects in distance based queries. iSPEED provides a 3D spatial query engine that can be invoked on-demand to run many instances in parallel implemented with, but not limited to, MapReduce. iSPEED builds in-memory indexes and decompresses data on-demand, which has minimal memory footprint. We evaluate iSPEED with two representative queries: 3D spatial joins and 3D spatial proximity estimation. Our experiments demonstrate that iSPEED significantly improves the performance over traditional non-memory based spatial query systems.

Efficiency Analysis of the access method with the cascading Bloom filter to the data warehouse on the parallel computing platform

A new method was developed with a cascading Bloom filter (CBF) for executing SQL queries in the Apache Spark parallel computing environment. It includes the representation of the original query in the form of several subqueries, the development of a connection graph and the transformation of subqueries, the definition of connections where it is necessary to use Bloom filters, the representation of the graph in terms of Spark. On the example of the query Q3 of the TPC-H test, full-scale experiments were carried out, which confirmed the effectiveness of the developed method.

An analytical study of large SPARQL query logs

With the adoption of RDF as the data model for Linked Data and the Semantic Web, query specification from end-users has become more and more common in SPARQL endpoints. In this paper, we conduct an in-depth analytical study of the queries formulated by end-users and harvested from large and up-to-date query logs from a wide variety of RDF data sources. As opposed to previous studies, ours is the first assessment on a voluminous query corpus, spanning over several years and covering many representative SPARQL endpoints. Apart from the syntactical structure of the queries, that exhibits already interesting results on this generalized corpus, we drill deeper in the structural characteristics related to the graph and hypergraph representation of queries. We outline the most common shapes of queries when visually displayed as undirected graphs, and characterize their (hyper-)tree width. Moreover, we analyze the evolution of queries over time, by introducing the novel concept of a streak, i.e., a sequence of queries that appear as subsequent modifications of a seed query. Our study offers several fresh insights on the already rich query features of real SPARQL queries formulated by real users, and brings us to draw a number of conclusions and pinpoint future directions for SPARQL query evaluation, query optimization, tuning, and benchmarking.

An analytical study of large SPARQL query logs

With the adoption of RDF as the data model for Linked Data and the Semantic Web, query specification from end-users has become more and more common in SPARQL endpoints. In this paper, we conduct an in-depth analytical study of the queries formulated by end-users and harvested from large and up-to-date query logs from a wide variety of RDF data sources. As opposed to previous studies, ours is the first assessment on a voluminous query corpus, spanning over several years and covering many representative SPARQL endpoints. Apart from the syntactical structure of the queries, that exhibits already interesting results on this generalized corpus, we drill deeper in the structural characteristics related to the graph and hypergraph representation of queries. We outline the most common shapes of queries when visually displayed as undirected graphs, and characterize their (hyper-)tree width. Moreover, we analyze the evolution of queries over time, by introducing the novel concept of a streak, i.e., a sequence of queries that appear as subsequent modifications of a seed query. Our study offers several fresh insights on the already rich query features of real SPARQL queries formulated by real users, and brings us to draw a number of conclusions and pinpoint future directions for SPARQL query evaluation, query optimization, tuning, and benchmarking.

A target recognition method for maritime surveillance radars based on hybrid ensemble selection

ABSTRACTIn order to improve the generalisation ability of the maritime surveillance radar, a novel ensemble selection technique, termed Optimisation and Dynamic Selection (ODS), is proposed. During the optimisation phase, the non-dominated sorting genetic algorithm II for multi-objective optimisation is used to find the Pareto front, i.e. a set of ensembles of classifiers representing different tradeoffs between the classification error and diversity. During the dynamic selection phase, the meta-learning method is used to predict whether a candidate ensemble is competent enough to classify a query instance based on three different aspects, namely, feature space, decision space and the extent of consensus. The classification performance and time complexity of ODS are compared against nine other ensemble methods using a self-built full polarimetric high resolution range profile data-set. The experimental results clearly show the effectiveness of ODS. In addition, the influence of the selection of diversity measures is studied concurrently.

Content-based image retrieval based on multiple extended fuzzy-rough framework

This paper presents a content-based image retrieval (CBIR) system with applications in one general purpose and two face image databases using two MPEG-7 image descriptors. The proposed method uses several sophisticated fuzzy-rough feature selection methods and combines the results of these methods to obtain a prominent feature subset for image representation for a particular query. Next, fuzzy-rough upper approximation of the target set (relevant list of images) with respect to the entire database that is represented by the prominent feature subset, is computed for retrieval and ranking. The information table on which every feature selection method works is small in size. Main reasons of performance boost of the proposed method are twofold. One is efficient feature subsets selection. The other reason is the fuzzy-indiscernibility relation based fuzzy-rough framework for computing upper-approximation which supports the approximate equality or similarity sense of CBIR. Fuzzy-rough upper approximation possibly adds more similar images in the relevant list from boundary region to expand the relevant list. The effectiveness of the proposed method is supported by the comparative results obtained from several single dimensionality reduction method, several clustering based retrieval techniques and also tested for face image retrieval.

Partial matching and search space reduction for QbE-STD

Query-by-Example approach of spoken content retrieval has gained much attention because of its feasibility in the absence of speech recognition and its applicability in a multilingual matching scenario. This approach to retrieve spoken content is referred to as Query-by-Example Spoken Term Detection (QbE-STD). The state-of-the-art QbE-STD system performs matching between the frame sequence of query and test utterance via Dynamic Time Warping (DTW) algorithm. In realistic scenarios, there is a need to retrieve the query which does not appear exactly in the spoken document. However, the appeared instance of query might have the different suffix, prefix or word order. The DTW algorithm monotonically aligns the two sequences and hence, it is not suitable to perform partial matching between the frame sequence of query and test utterance. In this paper, we propose novel partial matching approach between spoken query and utterance using modified DTW algorithm where multiple warping paths are constructed for each query and test utterance pair. Next, we address the research issue associated with search complexity of DTW and suggest two approaches, namely, feature reduction approach and Bag-of-Acoustic-Words (BoAW) model. In feature reduction approach, the number of feature vectors is reduced by averaging across the consecutive frames within phonetic boundaries. Thus, a lesser number of feature vectors require fewer number of comparisons and hence, DTW speeds up the search computation. The search computation time gets reduced by 46–49% with a slight degradation in performance as compared to no feature reduction case. In BoAW model, we construct term frequency-inverse document frequency (tf−idf) vectors at segment-level to retrieve audio documents. The proposed segment-level BoAW model is used to match test utterance with a query using (tf−idf) vectors and the scores obtained are used to rank the test utterance. The BoAW model gave more than 80% recall value on 70% top retrieval. To re-score the detection, we further employ DTW search or modified DTW search to retrieve the spoken query from the selected utterances using BoAW model. QbE-STD experiments are conducted on different international benchmarks, namely, MediaEval spoken web search SWS 2013 and MediaEval query-by-example search on speech QUESST 2014.

Exact Model Counting of Query Expressions

We prove exponential lower bounds on the running time of the state-of-the-art exact model counting algorithms—algorithms for exactly computing the number of satisfying assignments, or the satisfying probability, of Boolean formulas. These algorithms can be seen, either directly or indirectly, as building Decision-Decomposable Negation Normal Form (decision-DNNF) representations of the input Boolean formulas. Decision-DNNFs are a special case of d -DNNFs where d stands for deterministic . We show that any knowledge compilation representations from a class (called DLDDs in this article) that contain decision-DNNFs can be converted into equivalent Free Binary Decision Diagrams (FBDDs) , also known as Read-Once Branching Programs , with only a quasi-polynomial increase in representation size. Leveraging known exponential lower bounds for FBDDs, we then obtain similar exponential lower bounds for decision-DNNFs, which imply exponential lower bounds for model-counting algorithms. We also separate the power of decision-DNNFs from d -DNNFs and a generalization of decision-DNNFs known as AND-FBDDs. We then prove new lower bounds for FBDDs that yield exponential lower bounds on the running time of these exact model counters when applied to the problem of query evaluation in tuple-independent probabilistic databases—computing the probability of an answer to a query given independent probabilities of the individual tuples in a database instance. This approach to the query evaluation problem, in which one first obtains the lineage for the query and database instance as a Boolean formula and then performs weighted model counting on the lineage, is known as grounded inference . A second approach, known as lifted inference or extensional query evaluation , exploits the high-level structure of the query as a first-order formula. Although it has been widely believed that lifted inference is strictly more powerful than grounded inference on the lineage alone, no formal separation has previously been shown for query evaluation. In this article, we show such a formal separation for the first time. In particular, we exhibit a family of database queries for which polynomial-time extensional query evaluation techniques were previously known but for which query evaluation via grounded inference using the state-of-the-art exact model counters requires exponential time.

Querying biomedical Linked Data with natural language questions

Recent and intensive research in the biomedical area enabled to accumulate and disseminate biomedical knowledge through various knowledge bases increasingly available on the Web. The exploitation of this knowledge requires to create links between these bases and to use them jointly. Linked Data, the SPARQL language and interfaces in natural language question answering provide interesting solutions for querying such knowledge bases. However, while using biomedical Linked Data is crucial, life-science researchers may have difficulties using the SPARQL language. Interfaces based on natural language question answering are recognized to be suitable for querying knowledge bases. In this paper, we propose a method for translating natural language questions into SPARQL queries. We use Natural Language Processing tools, semantic resources and RDF triple descriptions. We designed a four-step method which allows to linguistically and semantically annotate questions, to perform an abstraction of these questions, then to build a representation of the SPARQL queries, and finally to generate the queries. The method is designed on 50 questions over three biomedical knowledge bases used in the task 2 of the QALD-4 challenge framework and evaluated on 27 new questions. It achieves good performance with 0.78 F-measure on the test set. The method for translating questions into SPARQL queries is implemented as a Perl module and is available at http://search.cpan.org/~thhamon/ RDF-NLP-SPARQLQuery/.

Entity Search Based on the Representation Learning Model With Different Embedding Strategies

We focus on the problem of learning distributed representations for entity search queries, named entities, and their short descriptions. With our representation learning models, the entity search query, named entity, and description can be represented as low-dimensional vectors with minimal human preprocessing. Our goal is to develop a simple but effective model that can make the distributed representations of query-related entities similar to the query representation in the vector space. Hence, we propose three kinds of learning strategies, and the difference between them mainly lies in how to deal with the relationship between an entity and its description. We analyze the strengths and weaknesses of each learning strategy and validate our methods on public data sets, which contain various query types and different languages (i.e., English and Chinese). The experimental results indicate that our proposed methods can adapt to different types of entity search queries, and outperform the current state-of-the-art methods no matter the entity collection is homogeneous or heterogeneous. Besides, the proposed methods can be trained fast and can be easily extended to other similar tasks.

A new ontology ranking method with OntoDUIA for ontology retrieval system

Ontology is being widely used for data integration and knowledge discovery in the field of data engineering. Ontology ranking is one of the important functions of ontology search engine, which ranks the searched ontologies in a reasonable order. A good ranking method can help a user acquiring the satisfied ontology efficiently from a considerable amount of search results. However, existing methods in literatures are unable to rank ontologies to well meet user's ranking demand due to their inherent shortcomings. In this paper, a novel ontology ranking method OntoDUIA is proposed and presented. It evaluates user-query related ontologies and ranks them based on a set of metrics of query relevance, usability, instance distribution and authority of ontology. To evaluate OntoDUIA, a series of experiments are conducted to compare the performance among OntoDUIA and some existing ontology ranking methods as well as human experts. Experimental results show that OntoDUIA can effectively meet user's ontology ranking demand, and it achieves stable and reliable ranking results. Finally, OntoDUIA can also be applied to ontology retrieval system.

Performance Optimization inTransactional Business Intelligence Applications’ Query-Generation Development Cycle

Objectives: To develop optimization techniques that can apply to any Transactional Business Intelligence (TBI) Applications development process leading to significant improvements in the performance and scalability of TBI queries. Methods/Statistical analysis: Benchmark experiments were conducted using Oracle RDBMS 11gR2 using representative SQL queries from Oracle’s Fusion TBI Applications. All four measures of SQL performance viz. SQL Response Time (RT), Input- Output (IO) Buffer Gets, Hard-Parse-Time and Shared Memory utilization were recorded with and without our proposed optimizations. The four performance measures were then compared to record actual improvements. Findings: Oracle TBI Applications are the result of a convergence of many technologies like the Application Development Framework (ADF),Web Logic Server (WLS) and Oracle Business Intelligence Enterprise Edition (OBI EE). TBI queries are generated at runtime by the OBI EE and ADF layers based on the transactional application schema, the ADF model and the logical, business and physical model layers of OBI EE metadata and optimizing this end to end process was the focus of our research. The benchmark experiments established very promising results. We recorded repeatable, significant gains in not across all measures of SQL performance. Our findings and recommendations can optimize both query and application performance. Adoption of our proposed solutions by OBI EE and TBI Fusion applications has provided significant performance and scalability improvements in enterprise OTBI applications. Even though our findings and recommendations were derived during a multi-year ADF-OBIEE application development environment, we are confident that these lessons would provide good guidance for developers embarking on architecting any new TBI applications. Application/Improvements: Ourproposed recommendations can improve performance of all TBI applications that use an abstracted model and metadata of the transactional data model with query-generation engines to generate TBI SQL queries.

Quantitative Methods for Similarity in Description Logics

Description logics (DLs) are a family of logic-based knowledge representation languages used to describe the knowledge of an application domain and reason about it in a formally well-defined way. However, all classical DLs have in common that they can only express exact knowledge, and correspondingly only allow exact inferences. In practice though, knowledge is rarely exact. Many definitions have exceptions or are vaguely formulated in the first place, and people might not only be interested in exact answers, but also in alternatives that are “close enough”. We are interested in tackling how to express that something is “close enough”, and how to integrate this notion into the formalism of DLs. To this end we employ the notion of similarity and dissimilarity measures, we will look at how useful measures can be defined in the context of DLs and two particular applications: Relaxed instance queries will use a similarity measure in order to not just give the exact answer to some query, but all answers that are reasonably similar. Prototypical definitions on the other hand use a measure of dissimilarity or distance between concepts in order to allow the definitions of and reasoning with concepts that capture not just those individuals that satisfy exactly the stated properties, but also those that are “close enough”.

Scalable 3D Spatial Queries for Analytical Pathology Imaging with MapReduce.

3D analytical pathology imaging examines high resolution 3D image volumes of human tissues to facilitate biomedical research and provide potential effective diagnostic assistance. Such approach - quantitative analysis of large-scale 3D pathology image volumes - generates tremendous amounts of spatially derived 3D micro-anatomic objects, such as 3D blood vessels and nuclei. Spatial exploration of such massive 3D spatial data requires effective and efficient querying methods. In this paper, we present a scalable and efficient 3D spatial query system for querying massive 3D spatial data based on MapReduce. The system provides an on-demand spatial querying engine which can be executed with as many instances as needed on MapReduce at runtime. Our system supports multiple types of spatial queries on MapReduce through 3D spatial data partitioning, customizable 3D spatial query engine, and implicit parallel spatial query execution. We utilize multi-level spatial indexing to achieve efficient query processing, including global partition indexing for data retrieval and on-demand local spatial indexing for spatial query processing. We evaluate our system with two representative queries: 3D spatial joins and 3D k-nearest neighbor query. Our experiments demonstrate that our system scales to large number of computing nodes, and efficiently handles data-intensive 3D spatial queries that are challenging in analytical pathology imaging.

Performance Optimization of Row-Level-Data-Security for Transactional Business Intelligence Queries in Cloud and SaaS Business Applications

Objectives: To optimize row-level data security for Transactional Business Intelligence (TBI) SQL queries to reduce complexity and enable the back-end database to create better optimized execution plans that perform and scale well. Methods/Statistical Analysis: Benchmark experiments were conducted using Oracle RDBMS 11gR2 using representative SQL queries from Oracle’s Fusion CRM TBI Applications for five different users with multiple, varying roles and data access permissions. All four measures of SQL performance viz. SQL response time (RT), Input-Output (IO) Buffer Gets, Hard-Parse-Time and Shared Memory utilization were recorded with and without our proposed optimizations. The four performance measures were then compared to record actual improvements. Findings: The benchmark experiments established very promising results. We recorded repeatable, significant gains in not only the four measures of individual SQL performance but also at the database resources level. The proposed architecture enables the creation of a hand-shake mechanism between the application and security frameworks to optimize query and application performance. This is done by creating name-value pairs of roles and filter criteria and passing these from the middleware/application to the security framework at run time. The result is a pruned version of the final physical SQL, retaining only relevant RDSPs while removing ones not logically needed. Such optimized row-level data security makes TBI SQL queries less complex and the back-end database is able to create better optimized execution plans that perform and scale well. Query Response Time (RT) improvements ranging from 5% to 745 times, Hard-Parse Time improvements from 1% to 208 times, Logical I/O or Buffer Gets’ improvement ranging from 43% to 454 times and SQL-Shared-Memory reduction by up to 52%. Application/Improvements: Our proposed architecture is directly applicable to improve performance of all TBI applications that use row-level data security, especially in the Software-as-a-Service (SaaS) and Cloud Models.