A design document for the CORAL deductive database system

CORAL is a deductive database system that supports a rich declarative language, and an interface to C++ that allows for a combination of declarative and imperative programming. It is important to know the design and the implementation for CORAL in order to be able to extend this system. This report documents the design for CORAL. The Unified Modeling Language (UML) is used to describe the Architecture, Structural Model and Behavioral Model. The design documentation is based on the study of the source code, the user manual and the published papers about CORAL

New IR & Ranking Algorithm for Top-K Keyword Search on Relational Databases ‘Smart Search’

Database management systems are as old as computers, and the continuous research and development in databases is huge and an interest of many database venders and researchers, as many researchers work in solving and developing new modules and frameworks for more efficient and effective information retrieval based on free form search by users with no knowledge of the structure of the database. Our work as an extension to previous works, introduces new algorithms and components to existing databases to enable the user to search for keywords with high performance and effective top-k results. Work intervention aims at introducing new table structure for indexing of keywords, which would help algorithms to understand the semantics of keywords and generate only the correct CN‟s (Candidate Networks) for fast retrieval of information with ranking of results according to user‟s history, semantics of keywords, distance between keywords and match of keywords. In which a three modules where developed for this purpose. We implemented our three proposed modules and created the necessary tables, with the development of a web search interface called „Smart Search‟ to test our work with different users. The interface records all user interaction with our „Smart Search‟ for analyses, as the analyses of results shows improvements in performance and effective results returned to the user. We conducted hundreds of randomly generated search terms with different sizes and multiple users; all results recorded and analyzed by the system were based on different factors and parameters. We also compared our results with previous work done by other researchers on the DBLP database which we used in our research. Our final result analysis shows the importance of introducing new components to the database for top-k keywords search and the performance of our proposed system with high effective results.نظم إدارة قواعد البيانات قديمة مثل أجيزة الكمبيوتر، و البحث والتطوير المستمر في قواعد بيانات ضخم و ىنالك اىتمام من العديد من مطوري قواعد البيانات والباحثين، كما يعمل العديد من الباحثين في حل وتطوير وحدات جديدة و أطر السترجاع المعمومات بطرق أكثر كفاءة وفعالية عمى أساس نموذج البحث الغير مقيد من قبل المستخدمين الذين ليس لدييم معرفة في بنية قاعدة البيانات. ويأتي عممنا امتدادا لألعمال السابقة، ويدخل الخوارزميات و مكونات جديدة لقواعد البيانات الموجودة لتمكين المستخدم من البحث عن الكممات المفتاحية )search Keyword )مع األداء العالي و نتائج فعالة في الحصول عمى أعمى ترتيب لمبيانات .)Top-K( وييدف ىذا العمل إلى تقديم بنية جديدة لفيرسة الكممات المفتاحية )Table Keywords Index ،)والتي من شأنيا أن تساعد الخوارزميات المقدمة في ىذا البحث لفيم معاني الكممات المفتاحية المدخمة من قبل المستخدم وتوليد فقط الشبكات المرشحة (s’CN (الصحيحة السترجاع سريع لممعمومات مع ترتيب النتائج وفقا ألوزان مختمفة مثل تاريخ البحث لممستخدم، ترتيب الكمات المفتاحية في النتائج والبعد بين الكممات المفتاحية في النتائج بالنسبة لما قام المستخدم بأدخالو. قمنا بأقتراح ثالث مكونات جديدة )Modules )وتنفيذىا من خالل ىذه االطروحة، مع تطوير واجية البحث عمى شبكة اإلنترنت تسمى "البحث الذكي" الختبار عممنا مع المستخدمين. وتتضمن واجية البحث مكونات تسجل تفاعل المستخدمين وتجميع تمك التفاعالت لمتحميل والمقارنة، وتحميالت النتائج تظير تحسينات في أداء استرجاع البينات و النتائج ذات صمة ودقة أعمى. أجرينا مئات عمميات البحث بأستخدام جمل بحث تم أنشائيا بشكل عشوائي من مختمف األحجام، باالضافة الى االستعانة بعدد من المستخدمين ليذه الغاية. واستندت جميع النتائج المسجمة وتحميميا بواسطة واجية البحث عمى عوامل و معايير مختمفة .وقمنا بالنياية بعمل مقارنة لنتائجنا مع االعمال السابقة التي قام بيا باحثون آخرون عمى نفس قاعدة البيانات (DBLP (الشييرة التي استخدمناىا في أطروحتنا. وتظير نتائجنا النيائية مدى أىمية أدخال بنية جديدة لفيرسة الكممات المفتاحية الى قواعد البيانات العالئقية، وبناء خوارزميات استنادا الى تمك الفيرسة لمبحث بأستخدام كممات مفتاحية فقط والحصول عمى نتائج أفضل ودقة أعمى، أضافة الى التحسن في وقت البحث

The efficient evaluation of visual queries within a logic-based framework

Bibliography: leaves 149-153.There has been much research in the area of visual query systems in recent years. This has stemmed from the need for a more powerful database visualization and querying ability. In addition, there has been a pressing need for a more intuitive interface for the non-expert user. Systems such as Hy+, developed at the University of Toronto, provide environments that satisfy a wide range of database interaction and querying, with the advantage of maintaining a visual interface abstraction throughout. This thesis explores issues related to the translation and evaluation of visual queries, including semantic and optimization possibilities. The primary focus will be on the GraphLog query language, defined in the context of the Hy+ visualization system. GraphLog is translated to the deductive database language Datalog, which is subsequently evaluated by the CORAL logic database system. We propose graph semantics, which define the meaning of visual queries in terms of paths in a graph, for monotone GraphLog. This provides a more intuitive meaning which is not linked to any particular translation. Therefore, Datalog generated by a translation may be compared to well-defined semantics to ensure that the translation preserves the intended meaning. By examining various queries in terms of the graph semantics, we uncover a shortcoming in the existing GraphLog translation. In addition, an alternative translation to Datalog, based on the construction of a nondeterministic finite state automaton, is described for GraphLog queries. The translation has the property that visual queries containing constants are optimized using a technique known as factoring. In addition, the translation performs an optimization on queries with multiple edges that contain no constants, referred to here as variable constraining

Deduktiv unterstützte Rekonstruktion biologischer Netzwerke aus flexibel analysierten Textdaten

Wallmeyer T. Deduktiv unterstützte Rekonstruktion biologischer Netzwerke aus flexibel analysierten Textdaten. Bielefeld: Universität Bielefeld; 2016

Provenance, Incremental Evaluation, and Debugging in Datalog

The Datalog programming language has recently found increasing traction in research and industry. Driven by its clean declarative semantics, along with its conciseness and ease of use, Datalog has been adopted for a wide range of important applications, such as program analysis, graph problems, and networking. To enable this adoption, modern Datalog engines have implemented advanced language features and high-performance evaluation of Datalog programs. Unfortunately, critical infrastructure and tooling to support Datalog users and developers are still missing. For example, there are only limited tools addressing the crucial debugging problem, where developers can spend up to 30% of their time finding and fixing bugs. This thesis addresses Datalog’s tooling gaps, with the ultimate goal of improving the productivity of Datalog programmers. The first contribution is centered around the critical problem of debugging: we develop a new debugging approach that explains the execution steps taken to produce a faulty output. Crucially, our debugging method can be applied for large-scale applications without substantially sacrificing performance. The second contribution addresses the problem of incremental evaluation, which is necessary when program inputs change slightly, and results need to be recomputed. Incremental evaluation allows this recomputation to happen more efficiently, without discarding the previous results and recomputing from scratch. Finally, the last contribution provides a new incremental debugging approach that identifies the root causes of faulty outputs that occur after an incremental evaluation. Incremental debugging focuses on the relationship between input and output and can provide debugging suggestions to amend the inputs so that faults no longer occur. These techniques, in combination, form a corpus of critical infrastructure and tooling developments for Datalog, allowing developers and users to use Datalog more productively

Scalable Logic Defined Static Analysis

Logic languages such as Datalog have been proposed as a method for specifying flexible and customisable static analysers. Using Datalog, various classes of static analyses can be expressed precisely and succinctly, requiring fewer lines of code than hand-crafted analysers. In this paradigm, a static analysis specification is encoded by a set of declarative logic rules and an o -the-shelf solver is used to compute the result of the static analysis. Unfortunately, when large-scale analyses are employed, Datalog-based tools currently fail to scale in comparison to hand-crafted static analysers. As a result, Datalog-based analysers have largely remained an academic curiosity, rather than industrially respectful tools. This thesis outlines our e orts in understanding the sources of performance limitations in Datalog-based tools. We propose a novel evaluation technique that is predicated on the fact that in the case of static analysis, the logical specification is a design time artefact and hence does not change during evaluation. Thus, instead of directly evaluating Datalog rules, our approach leverages partial evaluation to synthesise a specialised static analyser from these rules. This approach enables a novel indexing optimisations that automatically selects an optimal set of indexes to speedup and minimise memory usage in the Datalog computation. Lastly, we explore the case of more expressive logics, namely, constrained Horn clause and their use in proving the correctness of programs. We identify a bottleneck in various symbolic evaluation algorithms that centre around Craig interpolation. We propose a method of improving these evaluation algorithms by a proposing a method of guiding theorem provers to discover relevant interpolants with respect to the input logic specification. The culmination of our work is implemented in a general-purpose and highperformance tool called Souffl´e. We describe Souffl´e and evaluate its performance experimentally, showing significant improvement over alternative techniques and its scalability in real-world industrial use cases