This document summarizes a talk about standardizing the BSON serialization format used by MongoDB and Perl drivers. It discusses the challenges of serializing complex Perl data types to BSON, the need for wrapper classes, and efforts to standardize around the BSON and BSON::XS modules. Key points include extracting the MongoDB BSON codec into a configurable object, adapting BSON.pm to provide a standard codec API, and writing common tests to ensure compatibility between pure Perl and C extensions. The work aims to remove custom codecs and make MongoDB wrappers subclasses of standard BSON wrappers.
Apache AVRO is a data serialization system that includes a schema language, compact serialized format, RPC framework, and APIs in multiple languages. Its goals are to support cross-language data exchange and simple but expressive schema evolution. It provides a way to define data schemas and protocols and serialize data in a compact binary format. The serialization allows for dynamic schema evolution while preserving compatibility. AVRO integrates with Hadoop and provides tools for working with AVRO data and schemas. Version 1.3 is planned for release soon with evolving but stable APIs and formats.
The purpose of this talk will be to provide the audience with a 45 minute crash course in Typoscript.. The talk will be aimed at TYPO3 administrators and developers who find Typoscript frustrating, confusing, or downright maddening. I will start by explaining the fundamentals of TypoScript, including what it is, its syntax, and how it generally functions throughout the TYPO3 core. I will give a brief overview of available Typoscript resources on typo3.org and a quick lesson in how to read the TSREF, which can be a challenge in and of itself. Building off of this foundation, the talk will move quickly into more advanced Typoscript techniques and best practices. We’ll walk through of some of the more vexing components of Typoscript, including the ways in which Typoscript can interact with the page record (or, in some cases, the cObject data property), CASE objects, and more advanced parts of stdWrap. We’ll look at how, in the end, everything in TYPO3 gets rendered through Typoscript, including plugins. We’ll discuss strategies for extending the Typoscript that renders core content elements in CSS Styled Content. Everyone will leave the talk with a Typoscript cheat sheet, and better understanding of how to utilize Typoscript effectively in their TYPO3 projects and how to find solutions when Typoscript doesn’t do what they expect it to.
This document discusses different design options for modeling messaging inboxes in MongoDB. It describes three main approaches: fan out on read, fan out on write, and fan out on write with bucketing. Fan out on read involves storing a single document per message with all recipients, requiring a scatter-gather query to read an inbox. Fan out on write stores one document per recipient but still involves random I/O to read an inbox. Bucketed fan out on write stores inbox messages in arrays within "inbox" documents for each user, allowing an entire inbox to be read with one or two documents. This provides the best read performance while also distributing writes across shards. The document concludes that bucketed fan out on write is typically the better
The document provides an overview of schema design basics for document databases, including modeling goals, common data patterns like one-to-many and many-to-many relationships, and techniques for modeling tree structures.
This document provides an overview of the Resource Description Framework (RDF). It begins with background information on RDF including URIs, URLs, IRIs and QNames. It then describes the RDF data model, noting that RDF is a schema-less data model featuring unambiguous identifiers and named relations between pairs of resources. It also explains that RDF graphs are sets of triples consisting of a subject, predicate and object. The document also covers RDF syntax using Turtle and literals, as well as modeling with RDF. It concludes with a brief overview of common RDF tools including Jena.
This document provides an overview of JavaScript reversing techniques. It discusses JavaScript technologies like the DOM, Ajax, and JSON. It covers security aspects like the same-origin policy. It provides tips for analyzing JavaScript using tools like Firebug. It also demonstrates finding vulnerabilities like DOM-based XSS and reversing obfuscated JavaScript.
The document discusses the history and development of JSON (JavaScript Object Notation). It describes how Douglas Crockford discovered JSON in 2001, developed its specification with a simple one-page website, and then it was adopted widely without much promotion. JSON provided a useful format for browser/server communication and became very popular due to its simplicity, becoming a standard part of JavaScript.
This document summarizes a talk about standardizing the BSON serialization format used by MongoDB and Perl drivers. It discusses the challenges of serializing complex Perl data types to BSON, the need for wrapper classes, and efforts to standardize around the BSON and BSON::XS modules. Key points include extracting the MongoDB BSON codec into a configurable object, adapting BSON.pm to provide a standard codec API, and writing common tests to ensure compatibility between pure Perl and C extensions. The work aims to remove custom codecs and make MongoDB wrappers subclasses of standard BSON wrappers.
Apache AVRO is a data serialization system that includes a schema language, compact serialized format, RPC framework, and APIs in multiple languages. Its goals are to support cross-language data exchange and simple but expressive schema evolution. It provides a way to define data schemas and protocols and serialize data in a compact binary format. The serialization allows for dynamic schema evolution while preserving compatibility. AVRO integrates with Hadoop and provides tools for working with AVRO data and schemas. Version 1.3 is planned for release soon with evolving but stable APIs and formats.
The purpose of this talk will be to provide the audience with a 45 minute crash course in Typoscript.. The talk will be aimed at TYPO3 administrators and developers who find Typoscript frustrating, confusing, or downright maddening. I will start by explaining the fundamentals of TypoScript, including what it is, its syntax, and how it generally functions throughout the TYPO3 core. I will give a brief overview of available Typoscript resources on typo3.org and a quick lesson in how to read the TSREF, which can be a challenge in and of itself. Building off of this foundation, the talk will move quickly into more advanced Typoscript techniques and best practices. We’ll walk through of some of the more vexing components of Typoscript, including the ways in which Typoscript can interact with the page record (or, in some cases, the cObject data property), CASE objects, and more advanced parts of stdWrap. We’ll look at how, in the end, everything in TYPO3 gets rendered through Typoscript, including plugins. We’ll discuss strategies for extending the Typoscript that renders core content elements in CSS Styled Content. Everyone will leave the talk with a Typoscript cheat sheet, and better understanding of how to utilize Typoscript effectively in their TYPO3 projects and how to find solutions when Typoscript doesn’t do what they expect it to.
This document discusses different design options for modeling messaging inboxes in MongoDB. It describes three main approaches: fan out on read, fan out on write, and fan out on write with bucketing. Fan out on read involves storing a single document per message with all recipients, requiring a scatter-gather query to read an inbox. Fan out on write stores one document per recipient but still involves random I/O to read an inbox. Bucketed fan out on write stores inbox messages in arrays within "inbox" documents for each user, allowing an entire inbox to be read with one or two documents. This provides the best read performance while also distributing writes across shards. The document concludes that bucketed fan out on write is typically the better
The document provides an overview of schema design basics for document databases, including modeling goals, common data patterns like one-to-many and many-to-many relationships, and techniques for modeling tree structures.
This document provides an overview of the Resource Description Framework (RDF). It begins with background information on RDF including URIs, URLs, IRIs and QNames. It then describes the RDF data model, noting that RDF is a schema-less data model featuring unambiguous identifiers and named relations between pairs of resources. It also explains that RDF graphs are sets of triples consisting of a subject, predicate and object. The document also covers RDF syntax using Turtle and literals, as well as modeling with RDF. It concludes with a brief overview of common RDF tools including Jena.
This document provides an overview of JavaScript reversing techniques. It discusses JavaScript technologies like the DOM, Ajax, and JSON. It covers security aspects like the same-origin policy. It provides tips for analyzing JavaScript using tools like Firebug. It also demonstrates finding vulnerabilities like DOM-based XSS and reversing obfuscated JavaScript.
The document discusses the history and development of JSON (JavaScript Object Notation). It describes how Douglas Crockford discovered JSON in 2001, developed its specification with a simple one-page website, and then it was adopted widely without much promotion. JSON provided a useful format for browser/server communication and became very popular due to its simplicity, becoming a standard part of JavaScript.
MongoDB San Francisco 2013: Data Modeling Examples From the Real World presen...MongoDB
In this session, we'll examine schema design insights and trade-offs using real world examples. We'll look at three example applications: building an email inbox, selecting a shard key for a large scale web application, and using MongoDB to store user profiles. From these examples you should leave the session with an idea of the advantages and disadvantages of various approaches to modeling your data in MongoDB. Attendees should be well versed in basic schema design and familiar with concepts in the morning's basic schema design talk. No beginner topics will be covered in this session.
Scikits.learn (http://scikit-learn.sourceforge.net/) is a scikit for machine learning which has gained lots of popularity in recent months. In particular, it can be used for text and large scale database mining.
On another side, CubicWeb (http://www.cubicweb.org/) is a python-based framework for semantic web applications that has been used in different application fields (library, museum, conference, intranet applications).
The aim of this talk is to present how these tools can be used together for semantic data mining of rss feeds (clustering, prediction), and for building a news aggregator similar to google news.
Full description : http://www.euroscipy.org/talk/4291
This talk examines four real-world use cases for MongoDB document-based data modeling. We examine the implications of several possible solutions for each problem.
This document provides an introduction to JSON (JavaScript Object Notation), including what it is, its data structure, how to send and receive JSON data at both the client and server sides, and resources for further information. The key points covered are:
- JSON is a lightweight data format that is easy for humans and machines to read/write and is independent of programming languages.
- JSON data is structured as either a collection of name/value pairs (objects) or an ordered list of values (arrays).
- JSON can be converted to and from JavaScript objects using functions like eval() and JSON.parse().
- At the server, JSON data can be generated from objects and sent to clients, then parsed at the
NoSQL databases only unfold their entire strength when also embracing the their concepts regarding usage and schema design. These slides give some overview of features and concepts of MongoDB.
Building web applications with mongo db presentationMurat Çakal
The document introduces building web applications using MongoDB, a document-oriented database. It discusses MongoDB's data modeling and querying capabilities, including examples of modeling user and location data for a check-in application. The document also covers indexing, insertion, updating, and analytics queries for the sample location and user data models.
This document discusses rules and the Semantic Web Rule Language (SWRL). It defines rules as a means of representing knowledge similar to if-then statements. SWRL combines OWL and rule-based languages by allowing users to write rules that can refer to OWL classes, properties, individuals and datatypes. SWRL has an abstract and XML syntax and supports built-in predicates for manipulating data types. Rules provide more expressivity than RDFS and OWL in some cases, such as defining application behaviors, but rule-based reasoning is less performant so they should not be overused when RDFS/OWL suffice.
The document discusses schema design basics for MongoDB, including terms, considerations for schema design, and examples of modeling different types of data structures like trees, single table inheritance, and many-to-many relationships. It provides examples of creating indexes, evolving schemas, and performing queries and updates. Key topics covered include embedding data versus normalization, indexing, and techniques for modeling one-to-many and many-to-many relationships.
Schema Design by Example ~ MongoSF 2012hungarianhc
This document summarizes a presentation about schema design in MongoDB. It discusses embedding documents, linking documents through references, and using geospatial data for check-ins. Examples are given for modeling blog posts and comments, places with metadata, and user profiles with check-in histories. The document emphasizes designing schemas based on application needs rather than relational normalization.
The document discusses Elastic search queries. It covers the basic query process, types of queries including basic and complex queries. It then describes different methods for constructing queries including using URI parameters, request body JSON, and provides examples. It also outlines the standard request body structure and includes some common additional options like pagination.
The document provides an overview of validation of RDF data using the SHACL (Shapes Constraint Language) recommendation. It begins with background on RDF and then discusses why validation of RDF data is important. It introduces key SHACL concepts like shapes, constraints, targets, and property shapes. Examples are provided to illustrate node shapes, value type constraints, cardinality constraints, logical constraints, and property pair constraints. The document serves as an introduction to validating RDF data using the SHACL language.
The document discusses four data modeling use cases: message inboxes, history retention, indexed attributes, and multiple identities. It analyzes different schema designs for each use case, considering factors like query efficiency, write performance, and how well each option supports features like sharding and indexing. The conclusion emphasizes choosing a schema that balances query needs with write workload, reduces random I/O, and is tailored to the application's key use cases.
The document describes the Jena framework, which is a Java API for building semantic web and linked data applications. It allows for parsing, creating, querying and inferencing over RDF data. The key classes and interfaces in Jena include the Model interface for representing RDF graphs, classes for creating resources, properties and literals, interfaces for representing statements and querying models. Jena supports reading/writing RDF files, working with ontologies and rules, and includes a SPARQL query engine.
The document describes the InfoGrid graph database. It is composed of several parts including a RESTful GUI framework, semantic schemas, a graph database, and store implementations. It explains what a graph database is and how it differs from relational and other databases. Examples show how to create and relate nodes and edges in a graph using the InfoGrid API.
Jazoon 2010 - Building DSLs with EclipsePeter Friese
The document discusses building domain-specific languages (DSLs) with Eclipse. It introduces DSLs and their benefits, including raising the level of abstraction and focusing on solving specific problems. It then presents Xtext, an Eclipse framework for defining grammars and generating languages, parsers, and editors. Xtext allows defining a DSL using a grammar, which is then used to generate a meta-model, parser, and base editor infrastructure for the language.
The Gramsci Project is a multidisciplinary research project that aims to create a knowledge graph and facilitate browsing of information related to Antonio Gramsci's work. It involves developing semi-automatic annotation tools, integrating a triple store with a search interface, and experimenting with dynamically generated facets and rankings. The goal is to allow exploration of annotated texts and enable linking between related people, concepts and documents in Gramsci's body of work.
RABL is a Ruby gem that generates JSON, XML, and other data formats from Ruby objects for use in APIs. It allows developers to customize how objects are represented, including renaming attributes, nesting related objects, and only including certain fields conditionally. RABL uses a template format to define the structure and attributes of the serialized objects. Templates can also inherit from and extend each other to reduce duplication and support different representations of the same data.
Spark schema for free with David SzakallasDatabricks
DataFrames are essential for high-performance code, but sadly lag behind in development experience in Scala. When we started migrating our existing Spark application from RDDs to DataFrames at Whitepages, we had to scratch our heads real hard to come up with a good solution. DataFrames come at a loss of compile-time type safety and there is limited support for encoding JVM types.
We wanted more descriptive types without the overhead of Dataset operations. The data binding API should be extendable. Schema for input files should be generated from classes when we don’t want inference. UDFs should be more type-safe. Spark does not provide these natively, but with the help of shapeless and type-level programming we found a solution to nearly all of our wishes. We migrated the RDD code without any of the following: changing our domain entities, writing schema description or breaking binary compatibility with our existing formats. Instead we derived schema, data binding and UDFs, and tried to sacrifice the least amount of type safety while still enjoying the performance of DataFrames.
Scaling search to a million pages with Solr, Python, and Djangotow21
A talk given to DJUGL on the 26th July 2010, describing and introducing Solr, and discussing how we use it at Timetric to drive navigation across over a million dataseries.
The document discusses several key points about Python:
1. It summarizes praise for Python from programmers and companies like Google, NASA, and CCP Games, highlighting Python's simplicity, compactness, and ability to quickly develop applications.
2. It introduces common Python concepts like strings, lists, sequences, namespaces, polymorphism, and duck typing. Strings can be manipulated using slicing and methods. Lists and other sequences support indexing, slicing, and iteration.
3. Python uses name-based rather than type-based polymorphism through duck typing - an object's capabilities are defined by its methods and properties rather than its class.
The design, architecture, and tradeoffs of FluidDBTerry Jones
Slides from a talk given on May 22, 2009 at PGCon in Ottawa. Abstract and more details at http://www.pgcon.org/2009/schedule/events/176.en.html
Video available shortly.
MongoDB San Francisco 2013: Data Modeling Examples From the Real World presen...MongoDB
In this session, we'll examine schema design insights and trade-offs using real world examples. We'll look at three example applications: building an email inbox, selecting a shard key for a large scale web application, and using MongoDB to store user profiles. From these examples you should leave the session with an idea of the advantages and disadvantages of various approaches to modeling your data in MongoDB. Attendees should be well versed in basic schema design and familiar with concepts in the morning's basic schema design talk. No beginner topics will be covered in this session.
Scikits.learn (http://scikit-learn.sourceforge.net/) is a scikit for machine learning which has gained lots of popularity in recent months. In particular, it can be used for text and large scale database mining.
On another side, CubicWeb (http://www.cubicweb.org/) is a python-based framework for semantic web applications that has been used in different application fields (library, museum, conference, intranet applications).
The aim of this talk is to present how these tools can be used together for semantic data mining of rss feeds (clustering, prediction), and for building a news aggregator similar to google news.
Full description : http://www.euroscipy.org/talk/4291
This talk examines four real-world use cases for MongoDB document-based data modeling. We examine the implications of several possible solutions for each problem.
This document provides an introduction to JSON (JavaScript Object Notation), including what it is, its data structure, how to send and receive JSON data at both the client and server sides, and resources for further information. The key points covered are:
- JSON is a lightweight data format that is easy for humans and machines to read/write and is independent of programming languages.
- JSON data is structured as either a collection of name/value pairs (objects) or an ordered list of values (arrays).
- JSON can be converted to and from JavaScript objects using functions like eval() and JSON.parse().
- At the server, JSON data can be generated from objects and sent to clients, then parsed at the
NoSQL databases only unfold their entire strength when also embracing the their concepts regarding usage and schema design. These slides give some overview of features and concepts of MongoDB.
Building web applications with mongo db presentationMurat Çakal
The document introduces building web applications using MongoDB, a document-oriented database. It discusses MongoDB's data modeling and querying capabilities, including examples of modeling user and location data for a check-in application. The document also covers indexing, insertion, updating, and analytics queries for the sample location and user data models.
This document discusses rules and the Semantic Web Rule Language (SWRL). It defines rules as a means of representing knowledge similar to if-then statements. SWRL combines OWL and rule-based languages by allowing users to write rules that can refer to OWL classes, properties, individuals and datatypes. SWRL has an abstract and XML syntax and supports built-in predicates for manipulating data types. Rules provide more expressivity than RDFS and OWL in some cases, such as defining application behaviors, but rule-based reasoning is less performant so they should not be overused when RDFS/OWL suffice.
The document discusses schema design basics for MongoDB, including terms, considerations for schema design, and examples of modeling different types of data structures like trees, single table inheritance, and many-to-many relationships. It provides examples of creating indexes, evolving schemas, and performing queries and updates. Key topics covered include embedding data versus normalization, indexing, and techniques for modeling one-to-many and many-to-many relationships.
Schema Design by Example ~ MongoSF 2012hungarianhc
This document summarizes a presentation about schema design in MongoDB. It discusses embedding documents, linking documents through references, and using geospatial data for check-ins. Examples are given for modeling blog posts and comments, places with metadata, and user profiles with check-in histories. The document emphasizes designing schemas based on application needs rather than relational normalization.
The document discusses Elastic search queries. It covers the basic query process, types of queries including basic and complex queries. It then describes different methods for constructing queries including using URI parameters, request body JSON, and provides examples. It also outlines the standard request body structure and includes some common additional options like pagination.
The document provides an overview of validation of RDF data using the SHACL (Shapes Constraint Language) recommendation. It begins with background on RDF and then discusses why validation of RDF data is important. It introduces key SHACL concepts like shapes, constraints, targets, and property shapes. Examples are provided to illustrate node shapes, value type constraints, cardinality constraints, logical constraints, and property pair constraints. The document serves as an introduction to validating RDF data using the SHACL language.
The document discusses four data modeling use cases: message inboxes, history retention, indexed attributes, and multiple identities. It analyzes different schema designs for each use case, considering factors like query efficiency, write performance, and how well each option supports features like sharding and indexing. The conclusion emphasizes choosing a schema that balances query needs with write workload, reduces random I/O, and is tailored to the application's key use cases.
The document describes the Jena framework, which is a Java API for building semantic web and linked data applications. It allows for parsing, creating, querying and inferencing over RDF data. The key classes and interfaces in Jena include the Model interface for representing RDF graphs, classes for creating resources, properties and literals, interfaces for representing statements and querying models. Jena supports reading/writing RDF files, working with ontologies and rules, and includes a SPARQL query engine.
The document describes the InfoGrid graph database. It is composed of several parts including a RESTful GUI framework, semantic schemas, a graph database, and store implementations. It explains what a graph database is and how it differs from relational and other databases. Examples show how to create and relate nodes and edges in a graph using the InfoGrid API.
Jazoon 2010 - Building DSLs with EclipsePeter Friese
The document discusses building domain-specific languages (DSLs) with Eclipse. It introduces DSLs and their benefits, including raising the level of abstraction and focusing on solving specific problems. It then presents Xtext, an Eclipse framework for defining grammars and generating languages, parsers, and editors. Xtext allows defining a DSL using a grammar, which is then used to generate a meta-model, parser, and base editor infrastructure for the language.
The Gramsci Project is a multidisciplinary research project that aims to create a knowledge graph and facilitate browsing of information related to Antonio Gramsci's work. It involves developing semi-automatic annotation tools, integrating a triple store with a search interface, and experimenting with dynamically generated facets and rankings. The goal is to allow exploration of annotated texts and enable linking between related people, concepts and documents in Gramsci's body of work.
RABL is a Ruby gem that generates JSON, XML, and other data formats from Ruby objects for use in APIs. It allows developers to customize how objects are represented, including renaming attributes, nesting related objects, and only including certain fields conditionally. RABL uses a template format to define the structure and attributes of the serialized objects. Templates can also inherit from and extend each other to reduce duplication and support different representations of the same data.
Spark schema for free with David SzakallasDatabricks
DataFrames are essential for high-performance code, but sadly lag behind in development experience in Scala. When we started migrating our existing Spark application from RDDs to DataFrames at Whitepages, we had to scratch our heads real hard to come up with a good solution. DataFrames come at a loss of compile-time type safety and there is limited support for encoding JVM types.
We wanted more descriptive types without the overhead of Dataset operations. The data binding API should be extendable. Schema for input files should be generated from classes when we don’t want inference. UDFs should be more type-safe. Spark does not provide these natively, but with the help of shapeless and type-level programming we found a solution to nearly all of our wishes. We migrated the RDD code without any of the following: changing our domain entities, writing schema description or breaking binary compatibility with our existing formats. Instead we derived schema, data binding and UDFs, and tried to sacrifice the least amount of type safety while still enjoying the performance of DataFrames.
Scaling search to a million pages with Solr, Python, and Djangotow21
A talk given to DJUGL on the 26th July 2010, describing and introducing Solr, and discussing how we use it at Timetric to drive navigation across over a million dataseries.
The document discusses several key points about Python:
1. It summarizes praise for Python from programmers and companies like Google, NASA, and CCP Games, highlighting Python's simplicity, compactness, and ability to quickly develop applications.
2. It introduces common Python concepts like strings, lists, sequences, namespaces, polymorphism, and duck typing. Strings can be manipulated using slicing and methods. Lists and other sequences support indexing, slicing, and iteration.
3. Python uses name-based rather than type-based polymorphism through duck typing - an object's capabilities are defined by its methods and properties rather than its class.
The design, architecture, and tradeoffs of FluidDBTerry Jones
Slides from a talk given on May 22, 2009 at PGCon in Ottawa. Abstract and more details at http://www.pgcon.org/2009/schedule/events/176.en.html
Video available shortly.
This document summarizes a technical presentation about user content storage after JCR. It discusses limitations of Jackrabbit for social content and introduces the Sparse map concept for a highly concurrent, lock-free content store using sparse maps and distributed databases like Cassandra. Key features of Sparse include high concurrency, no synchronization, light-weight sessions and flat hierarchies for social content requirements.
This document summarizes lessons learned from building MongoDB and MongoEngine. Some key lessons include: dive in and start contributing to open source projects to help them progress; metclasses are an important tool that allow ORM functionality to be added to classes; not all new ideas are good and it's important to avoid straying too far from existing patterns that users expect; tracking changes at a granular level allows partial updates but adds complexity. Overall it encourages contributors to learn why certain approaches were taken and focus on improving existing designs rather than introducing radical changes.
Descriptive programming allows testers to directly enter object information into test scripts without using an object repository. There are two main types: static programming, where object information is directly provided in the script using properties and values, and dynamic programming, where description objects are created and used in the script. Descriptive programming is useful when objects are dynamic, the object repository is large, the application is not ready for recording, or modifications are needed but the repository is read-only. It offers benefits like faster execution, portability, easier maintenance, and the ability to start testing without the full application.
ESWC SS 2013 - Tuesday Keynote Steffen Staab: Programming the Semantic Webeswcsummerschool
This document discusses programming with semantic web data and Linked Open Data. It introduces SchemEX, a schema-level index for Linked Open Data that uses type clusters and bi-simulations to efficiently construct an index of the schema. It also discusses an application called LODatio that extends this index to support active user assistance for SPARQL queries, such as providing related queries, result snippets and references to relevant data sources. Finally, it introduces LiteQ, a language for integrating RDF types and queries into programming languages to allow exploring, programming and typing with semantic web data.
The document discusses programming techniques for the semantic web including exploring and querying RDF graphs through a language called LITEQ. LITEQ allows programmers to navigate RDF schemas to define types, retrieve object sets through queries, and define type conditions. The document also presents an indexing structure called SchemEx that can be used to efficiently search large RDF datasets by clustering schemas and entities.
This document introduces valid_model, a Python library for declarative data modeling. It allows defining data models using descriptors to specify data types and validation rules. This provides strict typing while remaining unopinionated about persistence. Custom descriptors can extend the library's functionality. The library aims to enable use cases like database modeling, form validation, and API request/response objects.
- The document discusses blind XML external entity (XXE) attacks against web applications. It provides background on the speaker and describes how XXE vulnerabilities can be exploited to read local files, scan internal networks, and access Windows network shares by abusing XML parser features.
- Several examples are given of exploiting XXE vulnerabilities using document type definitions (DTDs) and XML schema definitions (XSDs) to conduct blind attacks and extract information from external XML files without direct output of file contents. Challenges with these approaches are also outlined.
- Binary search techniques are proposed to more efficiently extract text from external files when only validation errors are returned. The document concludes by noting the relative rarity of XSD validation
1) The document provides an introduction to querying numismatic data using SPARQL including basic syntax, filtering, sorting, optional values, arithmetic functions, and visualization with Google Fusion Tables.
2) Examples are given for querying coin types, specimens, attributes, geographic findspots, and aggregating results.
3) Advanced techniques demonstrated include filtering by date, material, references, regular expressions, and spatial queries.
The document discusses JSON (JavaScript Object Notation), an open standard format used to transmit data between a server and applications. It describes how to convert JSON data to Swift types by decoding JSON into custom model objects using a JSONDecoder. It provides examples of decoding JSON into a dictionary of strings and decoding into a custom Report struct by implementing Codable and specifying coding keys. The document also discusses updating a URLSession data task completion handler to decode JSON data into a custom model object.
The document discusses programming techniques for the semantic web including LITEQ, a language for integrating RDF types and queries into programming languages. LITEQ allows programmers to navigate schemas, define types aligned with programming languages, and retrieve typed instances. The document also presents SchemEX, an index for efficiently searching RDF data sources in the linked open data cloud based on their schemas.
Similar to Test-driven development: a case study (20)
The document discusses common root causes of data center outages according to a survey, which found that the most frequent causes were UPS battery failure, UPS capacity being exceeded, accidental EPO/human error, and UPS equipment failure. The majority of respondents believed the outages could have been prevented by measures like improving equipment, increasing budgets and staffing, and performing preventative maintenance. Over half of organizations responded to outages by repairing, replacing, or purchasing additional IT or infrastructure equipment.
Monitoring tools are stuck in the 20th century and focused on static and reactive approaches like SNMP and ping checks. The future of monitoring requires a framework and toolbox of dynamic tools that can handle cloud environments, auto-scaling, configuration management and support passive monitoring, anomaly detection, and failure prediction through event data collection and analysis. References are provided for further reading on modernizing monitoring approaches.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help boost feelings of calmness, happiness and focus.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
From Natural Language to Structured Solr Queries using LLMsSease
This talk draws on experimentation to enable AI applications with Solr. One important use case is to use AI for better accessibility and discoverability of the data: while User eXperience techniques, lexical search improvements, and data harmonization can take organizations to a good level of accessibility, a structural (or “cognitive” gap) remains between the data user needs and the data producer constraints.
That is where AI – and most importantly, Natural Language Processing and Large Language Model techniques – could make a difference. This natural language, conversational engine could facilitate access and usage of the data leveraging the semantics of any data source.
The objective of the presentation is to propose a technical approach and a way forward to achieve this goal.
The key concept is to enable users to express their search queries in natural language, which the LLM then enriches, interprets, and translates into structured queries based on the Solr index’s metadata.
This approach leverages the LLM’s ability to understand the nuances of natural language and the structure of documents within Apache Solr.
The LLM acts as an intermediary agent, offering a transparent experience to users automatically and potentially uncovering relevant documents that conventional search methods might overlook. The presentation will include the results of this experimental work, lessons learned, best practices, and the scope of future work that should improve the approach and make it production-ready.
In our second session, we shall learn all about the main features and fundamentals of UiPath Studio that enable us to use the building blocks for any automation project.
📕 Detailed agenda:
Variables and Datatypes
Workflow Layouts
Arguments
Control Flows and Loops
Conditional Statements
💻 Extra training through UiPath Academy:
Variables, Constants, and Arguments in Studio
Control Flow in Studio
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
ScyllaDB is making a major architecture shift. We’re moving from vNode replication to tablets – fragments of tables that are distributed independently, enabling dynamic data distribution and extreme elasticity. In this keynote, ScyllaDB co-founder and CTO Avi Kivity explains the reason for this shift, provides a look at the implementation and roadmap, and shares how this shift benefits ScyllaDB users.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
What is an RPA CoE? Session 2 – CoE RolesDianaGray10
In this session, we will review the players involved in the CoE and how each role impacts opportunities.
Topics covered:
• What roles are essential?
• What place in the automation journey does each role play?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Must Know Postgres Extension for DBA and Developer during MigrationMydbops
Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
* Learn about crucial extensions like oracle_fdw, pgtt, and pg_audit that ease migration complexities.
* Gain valuable strategies for implementing these extensions in PostgreSQL to achieve license freedom.
* Discover how these key extensions can empower both developers and DBAs during the migration process.
* Don't miss this chance to gain practical knowledge from an industry expert and stay updated on the latest open-source database trends.
Mydbops Managed Services specializes in taking the pain out of database management while optimizing performance. Since 2015, we have been providing top-notch support and assistance for the top three open-source databases: MySQL, MongoDB, and PostgreSQL.
Our team offers a wide range of services, including assistance, support, consulting, 24/7 operations, and expertise in all relevant technologies. We help organizations improve their database's performance, scalability, efficiency, and availability.
Contact us: info@mydbops.com
Visit: https://www.mydbops.com/
Follow us on LinkedIn: https://in.linkedin.com/company/mydbops
For more details and updates, please follow up the below links.
Meetup Page : https://www.meetup.com/mydbops-databa...
Twitter: https://twitter.com/mydbopsofficial
Blogs: https://www.mydbops.com/blog/
Facebook(Meta): https://www.facebook.com/mydbops/
Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
"NATO Hackathon Winner: AI-Powered Drug Search", Taras KlobaFwdays
This is a session that details how PostgreSQL's features and Azure AI Services can be effectively used to significantly enhance the search functionality in any application.
In this session, we'll share insights on how we used PostgreSQL to facilitate precise searches across multiple fields in our mobile application. The techniques include using LIKE and ILIKE operators and integrating a trigram-based search to handle potential misspellings, thereby increasing the search accuracy.
We'll also discuss how the azure_ai extension on PostgreSQL databases in Azure and Azure AI Services were utilized to create vectors from user input, a feature beneficial when users wish to find specific items based on text prompts. While our application's case study involves a drug search, the techniques and principles shared in this session can be adapted to improve search functionality in a wide range of applications. Join us to learn how PostgreSQL and Azure AI can be harnessed to enhance your application's search capability.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
"Scaling RAG Applications to serve millions of users", Kevin GoedeckeFwdays
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...Fwdays
Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.