Linked Data storage solutions often optimize for low latency querying and quick responsiveness. Meanwhile, in the back-end, offline ETL processes take care of integrating and preparing the data. In this paper we explain a workflow and the results of a benchmark that examines which Linked Data storage solution and setup should be chosen for different dataset sizes to optimize the cost-effectiveness of the entire ETL process. The benchmark executes diversified stress tests on the storage solutions. The results include an in-depth analysis of four mature Linked Data solutions with commercial support and full SPARQL 1.1 compliance. Whereas traditional benchmarks studies generally deploy the triple stores on premises using high-end hardware, this benchmark uses publicly available cloud machine images for reproducibility and runs on commodity hardware. All stores are tested using their default configuration. In this setting Virtuoso shows the best performance in general. The other tree stores show competitive results and have disjunct areas of excellence. Finally, it is shown that each store’s performance heavily depends on the structural properties of the queries, giving an indication of where vendors can focus their optimization efforts.
Description:
ETL basically stands for Extract Transform Load - which simply implies the process where you extract data from Source Tables, transform them in to the desired format based on certain rules and finally load them onto Target tables. There are numerous tools that help you with ETL process - Informatica, Control-M being a few notable ones.
So ETL Testing implies - Testing this entire process using a tool or at table level with the help of test cases and Rules Mapping document.
In ETL Testing, the following are validated -
1) Data File loads from Source system on to Source Tables.
2) The ETL Job that is designed to extract data from Source tables and then move them to staging tables. (Transform process)
3) Data validation within the Staging tables to check all Mapping Rules / Transformation Rules are followed.
4) Data Validation within Target tables to ensure data is present in required format and there is no data loss from Source to Target tables.
Job Scope: 100% Job guarantee as this rare skill, many companies find crunch for candidates
Duration: Normal Track - 4 weekends
Fast Track – 2 weekends/2days
Fee: 8K
New Batch: Every weekend
Data Warehouse:
A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format.
Reconciled data: detailed, current data intended to be the single, authoritative source for all decision support.
Extraction:
The Extract step covers the data extraction from the source system and makes it accessible for further processing. The main objective of the extract step is to retrieve all the required data from the source system with as little resources as possible.
Data Transformation:
Data transformation is the component of data reconcilation that converts data from the format of the source operational systems to the format of enterprise data warehouse.
Data Loading:
During the load step, it is necessary to ensure that the load is performed correctly and with as little resources as possible. The target of the Load process is often a database. In order to make the load process efficient, it is helpful to disable any constraints and indexes before the load and enable them back only after the load completes. The referential integrity needs to be maintained by ETL tool to ensure consistency.
Description:
ETL basically stands for Extract Transform Load - which simply implies the process where you extract data from Source Tables, transform them in to the desired format based on certain rules and finally load them onto Target tables. There are numerous tools that help you with ETL process - Informatica, Control-M being a few notable ones.
So ETL Testing implies - Testing this entire process using a tool or at table level with the help of test cases and Rules Mapping document.
In ETL Testing, the following are validated -
1) Data File loads from Source system on to Source Tables.
2) The ETL Job that is designed to extract data from Source tables and then move them to staging tables. (Transform process)
3) Data validation within the Staging tables to check all Mapping Rules / Transformation Rules are followed.
4) Data Validation within Target tables to ensure data is present in required format and there is no data loss from Source to Target tables.
Job Scope: 100% Job guarantee as this rare skill, many companies find crunch for candidates
Duration: Normal Track - 4 weekends
Fast Track – 2 weekends/2days
Fee: 8K
New Batch: Every weekend
Data Warehouse:
A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format.
Reconciled data: detailed, current data intended to be the single, authoritative source for all decision support.
Extraction:
The Extract step covers the data extraction from the source system and makes it accessible for further processing. The main objective of the extract step is to retrieve all the required data from the source system with as little resources as possible.
Data Transformation:
Data transformation is the component of data reconcilation that converts data from the format of the source operational systems to the format of enterprise data warehouse.
Data Loading:
During the load step, it is necessary to ensure that the load is performed correctly and with as little resources as possible. The target of the Load process is often a database. In order to make the load process efficient, it is helpful to disable any constraints and indexes before the load and enable them back only after the load completes. The referential integrity needs to be maintained by ETL tool to ensure consistency.
This presenation explains basics of ETL (Extract-Transform-Load) concept in relation to such data solutions as data warehousing, data migration, or data integration. CloverETL is presented closely as an example of enterprise ETL tool. It also covers typical phases of data integration projects.
What is ETL testing & how to enforce it in Data WharehouseBugRaptors
Bugraptors always remains up to date with latest technologies and ongoing trends in testing. Technology like ELT Testing bringing the great changes which arises the scope of testing by keeping in mind all the positive and negative scenarios.
In this session we will do a tour of the recently released Isentris 4.0SP1 with Accelrys Draw 4.1. You will see Chemical Representation advances made available in this release; extended search and bond types (Dative, Haptic, H-Bond, Homology, Biologics – Self Contained Sequence Representation: SCSR, Markush - positional bond uncertainty). Isentris’ enhanced visualization components, the new Isentris for Excel renderer, and how Pipeline Pilot can now be seamless called within Isentris, opening a wide set of opportunities to bring new functionalities to the end-users.
Learn how Catalogic Software DPX Copy Data Services and Data Protection Solution can help you better meet your RPO's and RTO's with instant mountable snapshots
This talk describes the general architecture common to anomaly detections systems that are based on probabilistic models. By examining several realistic use cases, I illustrate the common themes and practical implementation methods.
Aligning Web Collaboration Tools with Research Data for ScholarsLaurens De Vocht
Resources for research are not always easy to explore, and
rarely come with strong support for identifying, linking and
selecting those that can be of interest to scholars. In this
work we introduce a model that uses state-of-the-art semantic technologies to interlink structured research data and data from Web collaboration tools, social media and Linked Open Data. We use this model to build a platform that connects scholars, using their proles as a starting point to explore novel and relevant content for their research. Scholars can easily adapt to evolving trends by synchronizing new social media accounts or collaboration tools and integrate then with new datasets. We evaluate our approach by a scenario of personalized exploration of research repositories where we analyze real world scholar profiles and compare them to a reference profile.
Using Triple Pattern Fragments To Enable Streaming of Top-k Shortest Paths vi...Laurens De Vocht
Searching for relationships between Linked Data resources is typically interpreted as a pathfinding problem: looking for chains of intermediary nodes (hops) forming the connection or bridge between these resources in a single dataset or across multiple datasets.
In many cases centralizing all needed linked data in a certain (specialized) repository or index to be able to run the algorithm is not possible or at least not desired. To address this, we propose an approach to top-k shortest pathfinding, which optimally translates a pathfinding query into se- quences of triple pattern fragment requests.
Triple Pattern Fragments were recently introduced as a solution to address the availability of data on theWeb and the scalability of linked data client applications, preventing data processing bottlenecks on the server.
The results are streamed to the client, thus allowing clients to do asynchronous processing of the top-k shortest paths.
We explain how this approach behaves using a training dataset, a subset of DBpedia with 10 million triples, and show the trade-offs to a SPARQL approach where all the data is gathered in a single triple store on a single machine.
Furthermore we investigate the scalability when increasing the size of the subset up to 110 million triples.
Software Package (NodeJS): npmjs.com/package/everything_is_connected_engine
Discovering Meaningful Connections between Resources in the Web of DataLaurens De Vocht
Slides of LDOW2013 presentation, May 14th, Rio De Janeiro, Brazil
We will show that semantically annotated paths lead to discovering meaningful, non-trivial relations and connections between multiple resources in large online datasets such as the Web of Data. Graph algorithms have always been key in pathfinding applications (e.g., navigation systems). They make optimal use of available computation resources to find paths in structured data. Applying these algorithms to Linked Data can facilitate the resolving of complex queries that involve the semantics of the relations between resources. In this paper, we introduce a new approach for finding paths in Linked Data that takes into account the meaning of the connections and also deals with scalability. An efficient technique combining pre-processing and indexing of datasets is used for finding paths between two resources in largedatasets within a couple of seconds. To demonstrate our approach, we have implemented a testcase using the DBpedia dataset.
Providing Interchangeable Open Data to Accelerate Development of Sustainable ...Laurens De Vocht
Travelers expect access to tourism information at anytime, anywhere, with any media. Mobile tourism guides, accessible via the Web, provide an omnipresent approach to this. Thereby it is expensive and not trivial to (re)model, translate and transform data over and over. This inhibits many players, including governments, in developing such applications. We report on our experience in running a project on mobile tourism in Flanders, Belgium where we develop a methodology and reusable formalization for the data disclosure. We apply open data standards to achieve a reusable and interoperable datahub for mobile tourism. We organized working groups resulting in a re-usable formal specification and serialization of the domain model that is immediately usable for building mobile tourism applications. This increased the awareness and lead to semantic convergence which is forming a regional foundation to develop sustainable mobile guides for tourism.
A Framework Concept for Profiling Researchers on Twitter using the Web of DataLaurens De Vocht
Based upon findings and results from our recent research we propose a generic frame-
work concept for researcher profiling with appliance to the areas of ”Science 2.0” and ”Research 2.0”. Intensive growth of users in social networks, such as Twitter generated a vast amount of information. It has been shown in many previous works that social networks users produce valuable content for profiling and recommendations. Our research focuses on identifying and locating experts for specific research area or topic. In our approach we apply semantic technologies like (RDF, SPARQL), common vocabularies (SIOC , FOAF, MOAT, Tag Ontology) and Linked Data (GeoNames , COLINDA).
A Visual Exploration Workflow as Enabler for the Exploitation of Linked Open ...Laurens De Vocht
Semantically annotating and interlinking Open Data results in Linked Open Data which concisely and unambiguously describes a knowledge domain.However, the uptake of the Linked Data depends on its usefulness to non-Semantic Web experts. Failing to support data consumers understanding the added-value of Linked Data and possible exploitation opportunities could inhibit its diffusion. In this paper, we propose an interactive visual workflow for discovering and exploring Linked Open Data. We implemented the workflow considering academic library metadata and carried out a qualitative evaluation. We assessed the workflow’s potential impact on data consumers which bridges the offer as published Linked Open Data, and the demand as requests for: (i) higher quality data; and (ii) more applications that re-use data. More than 70% of the 34 test users agreed that the workflow fulfills its goal: it facilitates non-Semantic Web experts to understand the potential of Linked Open Data
This presenation explains basics of ETL (Extract-Transform-Load) concept in relation to such data solutions as data warehousing, data migration, or data integration. CloverETL is presented closely as an example of enterprise ETL tool. It also covers typical phases of data integration projects.
What is ETL testing & how to enforce it in Data WharehouseBugRaptors
Bugraptors always remains up to date with latest technologies and ongoing trends in testing. Technology like ELT Testing bringing the great changes which arises the scope of testing by keeping in mind all the positive and negative scenarios.
In this session we will do a tour of the recently released Isentris 4.0SP1 with Accelrys Draw 4.1. You will see Chemical Representation advances made available in this release; extended search and bond types (Dative, Haptic, H-Bond, Homology, Biologics – Self Contained Sequence Representation: SCSR, Markush - positional bond uncertainty). Isentris’ enhanced visualization components, the new Isentris for Excel renderer, and how Pipeline Pilot can now be seamless called within Isentris, opening a wide set of opportunities to bring new functionalities to the end-users.
Learn how Catalogic Software DPX Copy Data Services and Data Protection Solution can help you better meet your RPO's and RTO's with instant mountable snapshots
This talk describes the general architecture common to anomaly detections systems that are based on probabilistic models. By examining several realistic use cases, I illustrate the common themes and practical implementation methods.
Aligning Web Collaboration Tools with Research Data for ScholarsLaurens De Vocht
Resources for research are not always easy to explore, and
rarely come with strong support for identifying, linking and
selecting those that can be of interest to scholars. In this
work we introduce a model that uses state-of-the-art semantic technologies to interlink structured research data and data from Web collaboration tools, social media and Linked Open Data. We use this model to build a platform that connects scholars, using their proles as a starting point to explore novel and relevant content for their research. Scholars can easily adapt to evolving trends by synchronizing new social media accounts or collaboration tools and integrate then with new datasets. We evaluate our approach by a scenario of personalized exploration of research repositories where we analyze real world scholar profiles and compare them to a reference profile.
Using Triple Pattern Fragments To Enable Streaming of Top-k Shortest Paths vi...Laurens De Vocht
Searching for relationships between Linked Data resources is typically interpreted as a pathfinding problem: looking for chains of intermediary nodes (hops) forming the connection or bridge between these resources in a single dataset or across multiple datasets.
In many cases centralizing all needed linked data in a certain (specialized) repository or index to be able to run the algorithm is not possible or at least not desired. To address this, we propose an approach to top-k shortest pathfinding, which optimally translates a pathfinding query into se- quences of triple pattern fragment requests.
Triple Pattern Fragments were recently introduced as a solution to address the availability of data on theWeb and the scalability of linked data client applications, preventing data processing bottlenecks on the server.
The results are streamed to the client, thus allowing clients to do asynchronous processing of the top-k shortest paths.
We explain how this approach behaves using a training dataset, a subset of DBpedia with 10 million triples, and show the trade-offs to a SPARQL approach where all the data is gathered in a single triple store on a single machine.
Furthermore we investigate the scalability when increasing the size of the subset up to 110 million triples.
Software Package (NodeJS): npmjs.com/package/everything_is_connected_engine
Discovering Meaningful Connections between Resources in the Web of DataLaurens De Vocht
Slides of LDOW2013 presentation, May 14th, Rio De Janeiro, Brazil
We will show that semantically annotated paths lead to discovering meaningful, non-trivial relations and connections between multiple resources in large online datasets such as the Web of Data. Graph algorithms have always been key in pathfinding applications (e.g., navigation systems). They make optimal use of available computation resources to find paths in structured data. Applying these algorithms to Linked Data can facilitate the resolving of complex queries that involve the semantics of the relations between resources. In this paper, we introduce a new approach for finding paths in Linked Data that takes into account the meaning of the connections and also deals with scalability. An efficient technique combining pre-processing and indexing of datasets is used for finding paths between two resources in largedatasets within a couple of seconds. To demonstrate our approach, we have implemented a testcase using the DBpedia dataset.
Providing Interchangeable Open Data to Accelerate Development of Sustainable ...Laurens De Vocht
Travelers expect access to tourism information at anytime, anywhere, with any media. Mobile tourism guides, accessible via the Web, provide an omnipresent approach to this. Thereby it is expensive and not trivial to (re)model, translate and transform data over and over. This inhibits many players, including governments, in developing such applications. We report on our experience in running a project on mobile tourism in Flanders, Belgium where we develop a methodology and reusable formalization for the data disclosure. We apply open data standards to achieve a reusable and interoperable datahub for mobile tourism. We organized working groups resulting in a re-usable formal specification and serialization of the domain model that is immediately usable for building mobile tourism applications. This increased the awareness and lead to semantic convergence which is forming a regional foundation to develop sustainable mobile guides for tourism.
A Framework Concept for Profiling Researchers on Twitter using the Web of DataLaurens De Vocht
Based upon findings and results from our recent research we propose a generic frame-
work concept for researcher profiling with appliance to the areas of ”Science 2.0” and ”Research 2.0”. Intensive growth of users in social networks, such as Twitter generated a vast amount of information. It has been shown in many previous works that social networks users produce valuable content for profiling and recommendations. Our research focuses on identifying and locating experts for specific research area or topic. In our approach we apply semantic technologies like (RDF, SPARQL), common vocabularies (SIOC , FOAF, MOAT, Tag Ontology) and Linked Data (GeoNames , COLINDA).
A Visual Exploration Workflow as Enabler for the Exploitation of Linked Open ...Laurens De Vocht
Semantically annotating and interlinking Open Data results in Linked Open Data which concisely and unambiguously describes a knowledge domain.However, the uptake of the Linked Data depends on its usefulness to non-Semantic Web experts. Failing to support data consumers understanding the added-value of Linked Data and possible exploitation opportunities could inhibit its diffusion. In this paper, we propose an interactive visual workflow for discovering and exploring Linked Open Data. We implemented the workflow considering academic library metadata and carried out a qualitative evaluation. We assessed the workflow’s potential impact on data consumers which bridges the offer as published Linked Open Data, and the demand as requests for: (i) higher quality data; and (ii) more applications that re-use data. More than 70% of the 34 test users agreed that the workflow fulfills its goal: it facilitates non-Semantic Web experts to understand the potential of Linked Open Data
Researcher Profiling based on Semantic Analysis in Social NetworksLaurens De Vocht
We propose a framework to address an important challenge in the context of the ongoing adoption of the “Web 2.0” in science and research, often referred to as “Research 2.0”. Microblogging is one of the trends with increasing leverage. The challenge in this thesis is to connect users of microblogging services such as Twitter based on specific common entities that are representative and truly matter to them. We investigated the possibilities of using social data for locating an expert who shares a very specific research topic. To enrich and verify this social data we link such content to existing open data provided by the online community. We are using semantic technologies (RDF ,SPARQL), com- mon ontologies (SIOC, FOAF, DublinCore, SWRC) and Linked Data (DBpedia, GeoNames, CoLinDa) to extract and mine the data about scientific conferences out of context of microblogs. We are identifying users related to each other based on entities such as topics (tags), events, time, locations and persons (mentions). As a proof-of-concept we explain, implement and evaluate such a researcher profiling use case. It involves the development of a framework that focuses on the proposition of researches based on topics and conferences they have in common. This framework provides an API that allows quick access to the analyzed information. A demonstration application: “Researcher Affinity Browser” shows how the API supports developers to build rich internet applications for Research 2.0. This application also intro- duces the concept “affinity” that exposes the implicit proximity between entities and users based on the content users produced. The usability of a demonstration application and the usefulness of the framework itself are investigated with an explicit evaluation question- naire. This user feedback lead to important conclusions about successful achievements and opportunities to further improve this effort.
Talend Community Use Group Bristol: Preparing your business for mastering dat...KETL Limited
This is the slide deck from the January 2016 Talend Community User Group Bristol hosted by KETL. The deck is from the presentation from Helen Woodcock on preparing your business case for MDM and Josh's worksheets for preparing a basic Talend look up table.
Benchmarking the Effectiveness of Associating Chains of Links for Exploratory...Laurens De Vocht
Linked Data offers an entity-based infrastructure to resolve indirect relations between resources, expressed as chains of links. If we could benchmark how effective retrieving chains of links from these sources is, we can motivate why they are a reliable addition for exploratory search interfaces. A vast number of applications could reap the benefits from encouraging insights in this field. Especially all kinds of knowledge discovery tasks related for instance to ad-hoc
decision support and digital assistance systems. In this paper, we explain a benchmark model for evaluating the effectiveness of associating chains of links with keyword-based queries. We illustrate the benchmark model with an example case using academic library and conference metadata where we measured precision involving targeted expert users and directed it towards search effectiveness. This kind of typical semantic search engine evaluation focusing on information
retrieval metrics such as precision is typically biased towards the final result only. However, in an exploratory search scenario, the dynamics of the intermediary links that could lead to potentially relevant discoveries are not to be neglected.
Effect of Heuristics on Serendipity in Path-Based Storytelling with Linked DataLaurens De Vocht
Path-based storytelling with Linked Data on the Web provides users the ability to discover concepts in an entertaining and educational way. Given a query context, many state-of-the-art pathfinding approaches aim at telling a story that coincides with the user’s expectations by investigating paths over Linked Data on the Web. By taking into account serendipity in storytelling, we aim at improving and tailoring existing approaches towards better fitting user expectations so that users are able to discover interesting knowledge without feeling unsure or even lost in the story facts. To this end, we propose to optimize the link estimation between - and the selection of facts in a story by increasing the consistency and relevancy of links between facts through additional domain delineation and refinement steps. In order to address multiple aspects of serendipity, we propose and investigate combinations of weights and heuristics in paths forming the essential building blocks for each story. Our experimental findings with stories based on DBpedia indicate the improvements when applying the optimized algorithm.
Each government level uses its own different information system. At the same time citizens expect that these governmental levels adopt a user-centric approach and provide instant access to their data or to open government data. Therefore the applications at various government levels need to be interoperable in support of the ‘once only-principle’: data is inputted and registered only once and then reused. Given government budget constraints and the cost and complexity of (re)modeling, translating and transforming data over and over, public administrations need to reduce interoperability costs. This is achieved by semantically aligning information between the different information systems of each government level. Semantical interoperable systems facilitate citizen-centered e-government services. This paper illustrates how the Open Standards for Linked Organizations program (OSLO) paved the way bottom-up from a broad basis of stakeholders towards a government-endorsed strategy. OSLO applied a generic process and methodology and provided practical insights on how to overcome the encountered hurdles: political support and adoption; reaching semantic agreement. The lessons learned in the region of Flanders (Belgium) can speed-up the process in other countries that face the complexity of integrating information intensive processes between different applications, administrations and government levels.
“Apache Hadoop, Now and Beyond”, Jim Walker, Director of Product Marketing, Hortonworks
Hadoop is an open source project that allows you to gain insight from massive amounts of structured and unstructured data quickly and without significant investment. It is shifting the way many traditional organizations think of analytics and business models. While it is deigned to take advantage of cheap commodity hardware, it is also perfect for the cloud as it is built to scale up or down without system interruption. In this presentation, Jim Walker will provide an overview of Apache Hadoop and its current state of adoption in and out of the cloud.
Talend Winter 17 enables IT to transform the data lake into qualified, clean data that anyone can use, so everyone can make more informed and faster decisions
The presentations covers mostly three key areas how Talend helps you get the most from your data lake.
Talend Data Preparation now has Big Data support so anyone can access trusted data in the lake and turn data into insight
New Talend Data Stewardship app helps IT and Business to collaborate on data quality problems and guide resolution. It empowers the business to ensure data integrity at the source.
3. And we all know that there is an amazing amount of innovation going on in the market today. Talend enables you to stay on the cutting edge of big data and cloud innovation with the flexibility to leverage pretty much anything out there in the market, such as Spark 2.0, AWS, Salesforce, MapR and more ….
La plateforme d'intégration de données de Talend dispose de nouvelles fonctionnalités de préparation et de gouvernance des données en libre service afin de transformer les data lakes en données qualifiées, propres et utilisables par tous
Building High Performance MySQL Query Systems and Analytic ApplicationsCalpont
This presentation describes how to build fast running MySQL applications that service read-based systems. It takes a special look at column databases and Calpont's InfiniDB
Building High Performance MySql Query Systems And Analytic Applicationsguest40cda0b
This presentation gives practical advice and tips on how to build high-performance read intensive databases, and discusses innovations such as column-oriented databases
"We can all agree that streaming is super cool. And for a while now, the adoption conversation has been largely led with an all-in mentality. But that’s silly. The only concerns end users have are:
-The freshness of their data
-Latency they require to meet their SLAs from source to consumption
-All while maintaining data quality and governance.
Luckily, the industry has realized this and we have seen a shift of streaming capabilities surfacing as an in-database technology, via objects as trivial to analytics engineers as views - materialized that is. With this convergence of streaming capabilities and batch level accessibility, this is when ELT tools like dbt can join in and expand out the adoption story.
dbt is the T in ELT, Extract Load and Transform. In dbt, analytics engineers design models - SQL (and occasional python) statements that encapsulate business logic. At runtime, dbt will wrap that logic in a DDL statement and send it over to the data platform to execute.
In this session, we’ll discuss how we see streaming at dbt Labs. We will dive into how we are extending dbt to support low-latency scenarios and the recent additions we have made to make batch and streaming allies in a DAG rather than archenemies."
SQL on Hadoop benchmarks using TPC-DS query setKognitio
Sharon Kirkham, VP Analytics & Consulting at Kognitio, ran the TPC-DS query set using Impala, SparkSQL and Kognitio, to test for speed, reliability and concurrency for different SQL on Hadoop solutions. Standard Hive was originally investigated as part of this benchmark but lack of SQL support and poor single thread performance meant it was removed.
Hadoop-DS: Which SQL-on-Hadoop Rules the HerdIBM Analytics
Originally Published on Oct 27, 2014
An overview of IBM's audited Hadoop-DS comparing IBM Big SQL, Cloudera Impala and Hortonworks Hive for performance and SQL compatibility. For more information, visit: http://www-01.ibm.com/software/data/infosphere/hadoop/
Data Warehouse Testing in the Pharmaceutical IndustryRTTS
In the U.S., pharmaceutical firms and medical device manufacturers must meet electronic record-keeping regulations set by the Food and Drug Administration (FDA). The regulation is Title 21 CFR Part 11, commonly known as Part 11.
Part 11 requires regulated firms to implement controls for software and systems involved in processing many forms of data as part of business operations and product development.
Enterprise data warehouses are used by the pharmaceutical and medical device industries for storing data covered by Part 11 (for example, Safety Data and Clinical Study project data). QuerySurge, the only test tool designed specifically for automating the testing of data warehouses and the ETL process, has been effective in testing data warehouses used by Part 11-governed companies. The purpose of QuerySurge is to assure that your warehouse is not populated with bad data.
In industry surveys, bad data has been found in every database and data warehouse studied and is estimated to cost firms on average $8.2 million annually, according to analyst firm Gartner. Most firms test far less than 10% of their data, leaving at risk the rest of the data they are using for critical audits and compliance reporting. QuerySurge can test up to 100% of your data and help assure your organization that this critical information is accurate.
QuerySurge not only helps in eliminating bad data, but is also designed to support Part 11 compliance.
Learn more at www.QuerySurge.com
Big Data Testing : Automate theTesting of Hadoop, NoSQL & DWH without Writing...RTTS
Testing of Hadoop, NoSQL and Data Warehouses Visually
-----------------------------------------------------------------------------
We just made automated data testing really easy. Automate your Big Data testing visually, with no programming needed.
See how to automate Hadoop, No SQL and Data Warehouse testing visually, without writing any SQL or HQL. See how QuerySurge, the leading Big Data testing solution, provides novices and non-technical team members with a fast & easy way to be productive immediately while speeding up testing for team members skilled in SQL/HQL.
This webinar is geared towards:
- Big Data & Data Warehouse Architects, ETL Developers
- ETL Testers, Big Data Testers
- Data Analysts
- Operations teams
- Business Intelligence (BI) Architects
- Data Management Officers & Directors
You will learn how to:
• Improve your Data Quality
• Accelerate your data testing cycles
• Reduce your costs & risks
• Realize a huge ROI
MIGRATION OF AN OLTP SYSTEM FROM ORACLE TO MYSQL AND COMPARATIVE PERFORMANCE ...cscpconf
Across the various RDBMS vendors Oracle has more than 60% [6] of market share, with a
complete feature-rich and secure offering. This has made Oracle as default choice as the
database choice for systems of all sizes.
There many open source databases as MySQL, PostgreS, etc. which has now evolved into
complete feature rich offerings and come with zero-licensing fee. This makes it an attractive
proposition to migrate from Oracle to an open-source distribution, to cut-down on licensing
costs.
Migrating an application from a commercial vendor to open source is based on typical
concerns of functionality and performabilty. Though there are various tools and offerings
available to migrate but currently there exists no reference points for the exact effort and impact of migration on the application. Thus we did a study of impact analysis and effort involved in migrating on OLTP application. We successfully migrated the application and did a performance comparison, which is covered in the paper. The paper also covers the tool and methodology used, along with the limitations of MySQL and presents learnings of the entire exercise.
Iod session 3423 analytics patterns of expertise, the fast path to amazing ...Rachel Bland
Session content from IBM Information On Demand 2013 provides an overview of the IBM Business Intelligence Pattern with BLU Acceleration and explains the underlying technology employed to deliver high speed analysis more quickly and easily than ever before.
Scaling Databricks to Run Data and ML Workloads on Millions of VMsMatei Zaharia
Keynote at Scale By The Bay 2020.
Cloud service developers need to handle massive scale workloads from thousands of customers with no downtime or regressions. In this talk, I’ll present our experience building a very large-scale cloud service at Databricks, which provides a data and ML platform service used by many of the largest enterprises in the world. Databricks manages millions of cloud VMs that process exabytes of data per day for interactive, streaming and batch production applications. This means that our control plane has to handle a wide range of workload patterns and cloud issues such as outages. We will describe how we built our control plane for Databricks using Scala services and open source infrastructure such as Kubernetes, Envoy, and Prometheus, and various design patterns and engineering processes that we learned along the way. In addition, I’ll describe how we have adapted data analytics systems themselves to improve reliability and manageability in the cloud, such as creating an ACID storage system that is as reliable as the underlying cloud object store (Delta Lake) and adding autoscaling and auto-shutdown features for Apache Spark.
QuerySurge Slide Deck for Big Data Testing WebinarRTTS
This is a slide deck from QuerySurge's Big Data Testing webinar.
Learn why Testing is pivotal to the success of your Big Data Strategy .
Learn more at www.querysurge.com
The growing variety of new data sources is pushing organizations to look for streamlined ways to manage complexities and get the most out of their data-related investments. The companies that do this correctly are realizing the power of big data for business expansion and growth.
Learn why testing your enterprise's data is pivotal for success with big data, Hadoop and NoSQL. Learn how to increase your testing speed, boost your testing coverage (up to 100%), and improve the level of quality within your data warehouse - all with one ETL testing tool.
This information is geared towards:
- Big Data & Data Warehouse Architects,
- ETL Developers
- ETL Testers, Big Data Testers
- Data Analysts
- Operations teams
- Business Intelligence (BI) Architects
- Data Management Officers & Directors
You will learn how to:
- Improve your Data Quality
- Accelerate your data testing cycles
- Reduce your costs & risks
- Provide a huge ROI (as high as 1,300%)
Get a clearer picture of potential cloud performance by looking beyond SPECra...Principled Technologies
When we ran various workloads on two Azure instances, the performance differences between the instances varied considerably and differed from SPECrate 2017 Integer scores
Testing Big Data: Automated Testing of Hadoop with QuerySurgeRTTS
Are You Ready? Stepping Up To The Big Data Challenge In 2016 - Learn why Testing is pivotal to the success of your Big Data Strategy.
According to a new report by analyst firm IDG, 70% of enterprises have either deployed or are planning to deploy big data projects and programs this year due to the increase in the amount of data they need to manage.
The growing variety of new data sources is pushing organizations to look for streamlined ways to manage complexities and get the most out of their data-related investments. The companies that do this correctly are realizing the power of big data for business expansion and growth.
Learn why testing your enterprise's data is pivotal for success with big data and Hadoop. Learn how to increase your testing speed, boost your testing coverage (up to 100%), and improve the level of quality within your data - all with one data testing tool.
Bridging the Last Mile: Getting Data to the People Who Need It (APAC)Denodo
Watch full webinar here: https://bit.ly/34iCruM
Many organizations are embarking on strategically important journeys to embrace data and analytics. The goal can be to improve internal efficiencies, improve the customer experience, drive new business models and revenue streams, or – in the public sector – provide better services. All of these goals require empowering employees to act on data and analytics and to make data-driven decisions. However, getting data – the right data at the right time – to these employees is a huge challenge and traditional technologies and data architectures are simply not up to this task. This webinar will look at how organizations are using Data Virtualization to quickly and efficiently get data to the people that need it.
Attend this session to learn:
- The challenges organizations face when trying to get data to the business users in a timely manner
- How Data Virtualization can accelerate time-to-value for an organization’s data assets
- Examples of leading companies that used data virtualization to get the right data to the users at the right time
1 SDEV 460 – Homework 4 Input Validation and BusineVannaJoy20
1
SDEV 460 – Homework 4
Input Validation and Business Logic Security Controls
Overview:
This homework will demonstrate your knowledge of testing security controls aligned with Input
validation and business logic. You will also use the recommended OWASP testing guide reporting format
to report your test findings.
Assignment: Total 100 points
Using the readings from weeks 7 and 8 as a baseline provide the following test and analysis descriptions
or discussion:
1. Testing for Reflected Cross site scripting (OTG-INPVAL-001)
The OWASP site list multiple approaches and examples for blackbox testing reflected XSS
vulnerabilities. In your own words, describe Reflected Cross Site scripting. Then, List and
describe 4 different examples that could be used for testing. Be sure to conduct additional
research for each example to provide your own unique test example. This most likely means you
will need to conduct some research on Javascript to make sure your syntax is correct.
2. Testing for Stored Cross site scripting (OTG-INPVAL-002)
The OWASP site list multiple approaches and examples for blackbox testing Stored XSS
vulnerabilities. In your own words, describe Stored Cross Site scripting. Then, List and describe 2
different examples that could be used for testing. Be sure to conduct additional research for
each example to provide your own unique test example. This most likely means you will need to
conduct some research on Javascript to make sure your syntax is correct.
3. Testing for SQL Injection (OTG-INPVAL-005)
SQL Injection remains a problem in applications yet could easily fixed. The following SQL
statement is in an HTML form as code with the $ variables directly input from the user.
SELECT * FROM Students WHERE EMPLID='$EMPLID' AND EMAIL='$email'
Would a form or application that includes this code be susceptible to SQL Injection? Why?
What specific tests would you perform to determine if the applications was vulnerable?
How would you fix this problem? Be specific be providing the exact code in a Language of your choice.
(e.g. Java, PHP, Python …)
4. Test business logic data validation (OTG-BUSLOGIC-001)
While reviewing some Java code, an analysis provided the following code snippets that contain
logic errors. For each example, describe the issue and provide code that would fix the logical
error:
a.
2
int x;
x = x + 1;
System.out.println("X = " + x);
b.
for (i=1; i<=5; i++) ; {
System.out.println("Number is " + i);
}
c.
if ( z > d) ; {
System.out.println("Z is bigger");
}
d.
String m1="one";
String m2="two";
if(m1 == m2) {
System.out.println(“M1 is equal to M2”);
}
e. The formula for the area of a trapezoid is:
A = (b1+b2)/2 * h
The following Java code is the implementation. Fix the logical error
double area;
double base1 = 2.3;
double base2 = 4.8;
double height = 12.5;
area = base1 + base2/2.0 * ...
Similar to Big Linked Data ETL Benchmark on Cloud Commodity Hardware (20)
Welocme to ViralQR, your best QR code generator.ViralQR
Welcome to ViralQR, your best QR code generator available on the market!
At ViralQR, we design static and dynamic QR codes. Our mission is to make business operations easier and customer engagement more powerful through the use of QR technology. Be it a small-scale business or a huge enterprise, our easy-to-use platform provides multiple choices that can be tailored according to your company's branding and marketing strategies.
Our Vision
We are here to make the process of creating QR codes easy and smooth, thus enhancing customer interaction and making business more fluid. We very strongly believe in the ability of QR codes to change the world for businesses in their interaction with customers and are set on making that technology accessible and usable far and wide.
Our Achievements
Ever since its inception, we have successfully served many clients by offering QR codes in their marketing, service delivery, and collection of feedback across various industries. Our platform has been recognized for its ease of use and amazing features, which helped a business to make QR codes.
Our Services
At ViralQR, here is a comprehensive suite of services that caters to your very needs:
Static QR Codes: Create free static QR codes. These QR codes are able to store significant information such as URLs, vCards, plain text, emails and SMS, Wi-Fi credentials, and Bitcoin addresses.
Dynamic QR codes: These also have all the advanced features but are subscription-based. They can directly link to PDF files, images, micro-landing pages, social accounts, review forms, business pages, and applications. In addition, they can be branded with CTAs, frames, patterns, colors, and logos to enhance your branding.
Pricing and Packages
Additionally, there is a 14-day free offer to ViralQR, which is an exceptional opportunity for new users to take a feel of this platform. One can easily subscribe from there and experience the full dynamic of using QR codes. The subscription plans are not only meant for business; they are priced very flexibly so that literally every business could afford to benefit from our service.
Why choose us?
ViralQR will provide services for marketing, advertising, catering, retail, and the like. The QR codes can be posted on fliers, packaging, merchandise, and banners, as well as to substitute for cash and cards in a restaurant or coffee shop. With QR codes integrated into your business, improve customer engagement and streamline operations.
Comprehensive Analytics
Subscribers of ViralQR receive detailed analytics and tracking tools in light of having a view of the core values of QR code performance. Our analytics dashboard shows aggregate views and unique views, as well as detailed information about each impression, including time, device, browser, and estimated location by city and country.
So, thank you for choosing ViralQR; we have an offer of nothing but the best in terms of QR code services to meet business diversity!
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Elizabeth Buie - Older adults: Are we really designing for our future selves?
Big Linked Data ETL Benchmark on Cloud Commodity Hardware
1. 1
Big Linked Data ETL Benchmark
on Cloud Commodity Hardware
iMinds – Ghent University
Dieter De Witte, Laurens De Vocht,
Ruben Verborgh, Erik Mannens, Rik Van de Walle
Ontoforce
Kenny Knecht, Filip Pattyn, Hans Constandt
4. 4
Introduction
Facilitate development of semantic federated query engine
close the (semantic) analytics gap in life sciences.
The query engine drives an exploratory search application: DisQover
Approach to federated querying by implementing ETL pipeline
indexes the user views in advance.
Combine Linked Open Data with private and licensed (proprietary) data
discovery of biomedical data
new insights in medicine development.
6. 6
Ensure minimal knowledge about data linking or annotation is
required
to explore and find results.
Write SPARQL directly
detailed knowledge of the predicates is required
might require first exploring to determine the URIs.
Scaling out to more data
Search queries are complex because search spans two distinct
domains:
1. the ‘space’ of clinical studies;
2. ‘drugs/chemicals’.
Challenges
8. 8
Approach
How to do federated search with
minimal latency for end-user?
Which RDF stores support the
infrastructure?
What aspects should the design
of a reusable benchmark take
into account?
9. 9
The scaling-out approach relies on low-end commodity
hardware but uses many nodes in a distributed system:
1. Specialized scalable RDF stores, the focus of this work;
2. Translating SPARQL and RDF to existing NoSQL stores;
3. Translating SPARQL and RDF to existing Big Data approaches
such as MapReduce, Impala, Apache Spark;
4. Distributing the data in physically separated SPARQL endpoints
over the Semantic Web, using federated querying techniques
to resolve complex questions.
Note: Compression (in-memory) is an alternative for distribution.
RDF datasets can be compressed (e.g. “Header Dictionary
Triples” – HDT).
Scaling out: techniques
11. 11
Typical DisQover queries introduce much query latency when directly
federated.
Facets consist of multiple separate SPARQL queries and serve both as
filter and as dashboard.
Data integration in DisQover:
Facets filter across all data originating from multiple different
sources.
Why?
13. 13
ETL
Design of benchmark focus:
ETL part needs to be optimally cost efficient.
SPARQL queries for indexes maximally aligned with
front-end.
What is are the tradeoffs for each RDF store?
Benchmark
14. 14
What is the most cost-effective storage solution to support Linked Data
applications that need to be able to deal with heavy ETL query
workloads?
Which performance trade-offs do storage solutions offer in terms of
scalability?
What is the impact of different query types (templates)?
Is there a difference in performance between the stores based on the
structural properties of the queries?
Note: not taken into account implicitly derived facts, inference or reasoning.
Questions the benchmark answers
15. 15
WatDiv provides stress testing tools for SPARQL
existing benchmarks not always suitable for testing systems in
diverse queries and varied workloads:
generic benchmark + not application specific;
covers a broad spectrum
result cardinality
triple-pattern selectivity
ensured through the data and query generation method;
Benchmark is repeatable with different dataset sizes or numbers of
queries.
Data and Query Generation
16. 16
The RDF store should be capable of serving in a production environment with
Linked Data in Life Sciences.
The initial selection was made by choosing stores with:
• a high adoption/popularity as defined by DB-Engines.com ranking for RDF
stores;
• enterprise support;
• support for distributed deployment;
• full SPARQL 1.1 compliance.
The four stores we selected all comply with these constraints.
Note: The names of two stores we tested could not be disclosed.
They are being referred to as Enterprise Store I and II (ESI and ESII)
RDF Store Selection
17. 17
The benchmark process consists of a data loading phase, followed by
running the SPARQL benchmarker:
1. The data is loaded in compressed format (gzip).
2. The benchmarker runs in multi-threaded mode (8 threads),
runs a set of 2000 queries multiple times.
3. These runs consists of at least one warm-up run which is not
counted.
4. In order to obtain robust results the tail results (most extreme) are
discarded before calculating average query runtimes.
5. The benchmarker generates a CSV file containing the run times
and response times etc. of all queries which we visualized.
Process
18. 18
Query Driver
“SPARQL Query Benchmarker” is a general purpose API and CLI that is
designed primarily for testing remote SPARQL servers.
By default operations are run in a random order to avoid the system under
test (SUT) learning the pattern of operations.
Hardware
Executed all benchmarks on the Amazon Web Services (AWS) Elastic
Compute Cloud (EC2) and Simple Storage Solutions (S3).
Used the default (commercial) deployments of the SUT for the results to
be reproducible:
both the hardware and the machine images can be easily acquired.
more generally, cloud deployments offer the advantage of not
requiring dedicated on-premises hardware.
Infrastructure
26. 26
Errors and time-outs
Every runtime > 300s is a time-out.
If the run-time reaches a maximum of < 300s we detect an internal set time-
out.
This was in particular the case voor ESII (3 nodes)
60
28. 28
Issues in the followed approach
Choose for virtual machine images in the cloud (AWS) for
reproducibility;
but cloud solutions might not always be best suited for production.
The results of different benchmark studies might depend on many
(hidden) configuration factors leading to different or even
contradicting results.
The difference in performance between the stores might be attributed
to the use of commodity hardware in the cloud.
Differences partially attributed to the quality of the recommended
configuration parameters as provided by the virtual machine images.
30. 30
Conclusions & Next steps
Compared enterprise RDF stores
default configuration
without the intervention of enterprise support.
Run stores in their optimal configuration (reflecting a production
setting)
with more instances (> 3).
Repeat the benchmark with DisQover data and queries.
Create overview of RDF solutions for different
use cases, configurations and real-world (life science) datasets.
Investigate whether the WatDiv results are confirmed when running the
benchmark with other queries and data.
Release tools for repeating the benchmark with new storage solutions.
1. Title slide
2. Problem: What is the problem that you are addressing and why the problem is important? Who will benefit if you succeed? Who should care?
3. State of the art: Why is the problem difficult? What have others tried to do?
4. Research questions and hypothesis: What is the object of your study? What is the hypothesis that you will test?
5. Preliminary results: Do you have any preliminary results that demonstrate that your approach is promising 6. Your approach: What is the main idea behind your approach? The key innovation?
7. Evaluation plan: How do you plan to test your hypothesis? What will you measure? What will you compare to?
8. Reflections: Provide an argument, based either on common knowledge or on evidence that you have accumulated, the your approach is likely to succeed.
9. Lessons Learned: Summarize the lessons that you have learned so far. Discuss the positive and negative results that you have observed.
1. Title slide
2. Problem: What is the problem that you are addressing and why the problem is important? Who will benefit if you succeed? Who should care?
3. State of the art: Why is the problem difficult? What have others tried to do?
4. Research questions and hypothesis: What is the object of your study? What is the hypothesis that you will test?
5. Preliminary results: Do you have any preliminary results that demonstrate that your approach is promising 6. Your approach: What is the main idea behind your approach? The key innovation?
7. Evaluation plan: How do you plan to test your hypothesis? What will you measure? What will you compare to?
8. Reflections: Provide an argument, based either on common knowledge or on evidence that you have accumulated, the your approach is likely to succeed.
9. Lessons Learned: Summarize the lessons that you have learned so far. Discuss the positive and negative results that you have observed.
1. Title slide
2. Problem: What is the problem that you are addressing and why the problem is important? Who will benefit if you succeed? Who should care?
3. State of the art: Why is the problem difficult? What have others tried to do?
4. Research questions and hypothesis: What is the object of your study? What is the hypothesis that you will test?
5. Preliminary results: Do you have any preliminary results that demonstrate that your approach is promising 6. Your approach: What is the main idea behind your approach? The key innovation?
7. Evaluation plan: How do you plan to test your hypothesis? What will you measure? What will you compare to?
8. Reflections: Provide an argument, based either on common knowledge or on evidence that you have accumulated, the your approach is likely to succeed.
9. Lessons Learned: Summarize the lessons that you have learned so far. Discuss the positive and negative results that you have observed.
1. Title slide
2. Problem: What is the problem that you are addressing and why the problem is important? Who will benefit if you succeed? Who should care?
3. State of the art: Why is the problem difficult? What have others tried to do?
4. Research questions and hypothesis: What is the object of your study? What is the hypothesis that you will test?
5. Preliminary results: Do you have any preliminary results that demonstrate that your approach is promising 6. Your approach: What is the main idea behind your approach? The key innovation?
7. Evaluation plan: How do you plan to test your hypothesis? What will you measure? What will you compare to?
8. Reflections: Provide an argument, based either on common knowledge or on evidence that you have accumulated, the your approach is likely to succeed.
9. Lessons Learned: Summarize the lessons that you have learned so far. Discuss the positive and negative results that you have observed.
1. Title slide
2. Problem: What is the problem that you are addressing and why the problem is important? Who will benefit if you succeed? Who should care?
3. State of the art: Why is the problem difficult? What have others tried to do?
4. Research questions and hypothesis: What is the object of your study? What is the hypothesis that you will test?
5. Preliminary results: Do you have any preliminary results that demonstrate that your approach is promising 6. Your approach: What is the main idea behind your approach? The key innovation?
7. Evaluation plan: How do you plan to test your hypothesis? What will you measure? What will you compare to?
8. Reflections: Provide an argument, based either on common knowledge or on evidence that you have accumulated, the your approach is likely to succeed.
9. Lessons Learned: Summarize the lessons that you have learned so far. Discuss the positive and negative results that you have observed.
There is no clear second place. Whereas ESI performs well on small
datasets, Blazegraph shows better results for larger datasets. ESII’s
performance on a single instance benchmark is worse than the others,
but its claim of being highly scalable is confirmed in a configuration
with three instances where it performs significantly better. All data
stores have acceptible results for 10 and 100 million triples and
the choice to go by one or the other could depend on additional
features each of the stores has to offer such as support for full-text
indexing, support for linked data fragment interfaces or superior
automatic inferencing. For the larger datasets Virtuoso should be
the first choice as a single instance solution. The initial results in
a distributed setup with ESII shows promising results in terms of
scaling out, proving that this store’s power might only be revealed in
large multi-instance benchmarks.
1. Title slide
2. Problem: What is the problem that you are addressing and why the problem is important? Who will benefit if you succeed? Who should care?
3. State of the art: Why is the problem difficult? What have others tried to do?
4. Research questions and hypothesis: What is the object of your study? What is the hypothesis that you will test?
5. Preliminary results: Do you have any preliminary results that demonstrate that your approach is promising 6. Your approach: What is the main idea behind your approach? The key innovation?
7. Evaluation plan: How do you plan to test your hypothesis? What will you measure? What will you compare to?
8. Reflections: Provide an argument, based either on common knowledge or on evidence that you have accumulated, the your approach is likely to succeed.
9. Lessons Learned: Summarize the lessons that you have learned so far. Discuss the positive and negative results that you have observed.