ISS Art
Top Software Development Company in Omsk, Russia
162 visitsISS Art About
ISS Art It’s your chance to break new ground in business!
$25 - $49/hr
We are ISS Art and we can implement all your challenging ideas. No matter if you want to build a new product or enhance an existing one, we are here to help you! Since 2003 we have made over 500 clients all over the world happy! From startup founders and mid-size business owners to presidents of global corporations in such areas as production, media, healthcare and FMCG. You can rely on our expertise in ML and AI. We'll allocate the right team exactly for your
2003
50 - 249
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ISS Art Reviews
Lachlan Tetlow-Stuart
A high quality experience, well managed and with solid deliverables
Although we ended the project before finding a solution, based on changes to the scope of the project, our experience with ISS Art was great. The project was managed well and we were updated regularly on the progress. If a similar project comes along, we'll be sure to reach out to them again.
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ISS Art Services
- Python
- Data Analytics
- Predictive Analytics
- Deep Learning
- Hybrid Cloud
- SaaS
- Data Visualization
- Data Science
ISS Art Portfolio
Cassantec prognostic solution
Cassantec is an industry-leading provider of a SaaS-solution that uses unique mathematical algorithms to forecast equipment malfunctions. This allows businesses (nuclear power, fossil power, railways, etc.) to make timely decisions to repair or replace equipment depending on forecast results and save millions of dollars. The prognostic solution is the only solution which provides reasonably accurate forecasts for 3 years ahead. It defines the level of malfunctions: standard, high, critical. The forecasts are based on data analysis of a great number of sensor controllers not on the analysis of past malfunctions that other systems make. Challenges Development challenges: Cassantec’s customers had difficulties working with large amounts of data. The reports couldn’t be viewed in a browser. Cassentec wanted to make the report generation in the backend faster. The problems on the server caused instability of the backend. QA challenges: This is a big data application. It processes a huge amount of data related to the forecast of possible equipment malfunction.The equipment for which the forecast is calculated may include a number of units, components. Each of them may have a number of parameters, monitored and sampled over the time,on which the prognosis is calculated. The primary challenge for QA team was to understand the business domain and understand how the configuration of the monitored equipment is described. Then to create test equipment models with sets of units and components and to craft the test parameter datasets. Solutions The frontend needed to be optimized. Then the reports could have been viewed in the browser on Cassantec site. For speeding the report generation some calculations are made in the background (so called pre-computing results). We optimized the code and tuned GarbageCollection. QA activities: Functional testing, API testing, performance testing,configurational testing, web Automation, QA process establishment and management. QA tools: Selenium, Browser DevTools, MySQL Workbench, Putty, JIRA,TestLink, Blazemeter. Results We have optimized the frontend and the reports can be viewed in the browser. We have shared our experience and written an article about frontend optimization in the blog. We have added a new feature - Machine Learning. And the reports are now generated faster. The system works stably and Casantec’s customers save dozens of million dollars. ISS Art continues to assist in optimizing the work on the project as it is a long-term project. Technologies: Java, JavaScript, Scala Areas of expertise: browser graphic, mathematical statistics and probability theory, cloud computing, flexible configuration, forecasting, JSON data format, PDF-report generating, distributed computing, service Integration Duration: 5 years Team: 11 employees
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Not Disclosed
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100 weeks
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Manufacturing
SaaS solution for managing construction and agriculture sites
The web application has been developed for a global leading provider of advanced location-based solutions (positioning systems) — Trimble Navigations. The system has 21’755’588 weekly queries with support of complex search results exceeding 270 searches/second in production. The project is a web-portal for managing manufacturing process in providing a wide range of solutions: adjusting Client’s own web interface of an application using the modules offered by the system, creating future construction projects, approving documents, managing virtual filesystem. This system is applied in construction, agriculture and other industries. Challenges Development challenges: The client needed to considerably enlarge the system and itsdatabase. Large volumes of data had to be migrated from theserver to Cloud. The client faced the problem of lowproductivity. The downtime was longer than 30 seconds. The server deployment should be simplified and alldependencies on a great number of technologies should beexcluded. With the project growth the number of users asking foradditional modules increased. These modules needed to beimplemented at the user end. QA challenges: This is the SaaS application with desktop, mobile and webclients. The application has its own virtual file system thatstores various user contents. The application users havedifferent roles in the application and different permissions forapplication objects and contents. The application has manythird party plugins with complex business logic for specificindustries. The application has a few backend services,which perform time and resource consuming actions, whichare configured with numerous backend parameters. Theapplication can be classified as a Big Data application. A realchallenge was the development of test data, which wouldinclude users with various roles, multiple types of objectsand contents with assigned permissions for different typesof users. We had to take into account that the data could besynchronized to users desktop with the application desktopclient. The application UI was translated to 15 languages and aseach iteration there were changes in front end, we had toconduct localization testing. We had to develop a specialprocess for testing localization and involve translators in it. The development and QA teams of the project weredistributed between 3 offices, located in the different timezones, so the QA processes we developed took into accountthis fact. The project was featured by a long iteration period, so eachrelease consisted of a number of new features and fixedbugs. We introduced to the QA process the impact analysis,which we conducted before release testing in order tooptimize the testing efforts. The application has the comprehensive documentation andsince throughout the development it was constantly updated,QA team was involved in the review of the applicationdocumentation and manuals. Solutions For solving problems of low productivity and databasemigration our specialists used a complex approach: theyintroduced Amazon Web Services and created their ownfilesystem that improved the whole system. Our developers used Quercus (http://quercus.caucho.com/)and integrated it with the server. We created a new feature Application service that makes itpossible to add third parties’ modules. UI modules can beintegrated with the web-portal, a business logic is written inGroovy and it is available as REST API. QA activities: Functional testing, load testing, usability testing, localizationtesting, configurational testing, compatibility testing, securitytesting, web Automation, QA process establishment andmanagement. QA tools: Selenium, Browser DevTools, Postman, Apache JMeter, MySQLWorkbench, Putty, Fiddler, JIRA, Zephyre, SwaggerUI,Blazemeter, Datadog, NewRelic, Loggly, SauceLabs, Nessusscanner. Results The client needed to considerably enlarge the system and its database. Large volumes of data had tobe migrated from the server to Cloud. The client faced the problem of low productivity. The downtimewas longer than 30 seconds. The server deployment should be simplified and all dependencies on a great number of technologiesshould be excluded. With the project growth the number of users asking for additional modules increased. These modulesneeded to be implemented at the user end. Technologies: Java, JavaScript, C++, C#, Groovy, Spring, Hibernate, Mysql Areas of expertise: high load systems, scalability, big data, failover, multithreading, AWS administration (EC2; S3; RDS; ELB; CloudFront; ElasticCache; Route53; CloudWatch), continuous integration bamboo, localization, fulltext search using Solr engine, dynamically extensible UI and backend, secure execution of external Groovy scripts Duration: 8 years Team: 33 employees
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Not Disclosed
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100 weeks
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Manufacturing
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