Case Studies

CUSTOMER

Invention Machine is the leading provider of software that speeds the process of innovation. For more than a decade, Invention Machine has helped over 1,000 companies worldwide, to drive top-line growth, reduce costs, and speed up time-to-market by improving process efficiencies and more rapidly fueling their product pipelines with higher-quality products.

CHALLENGE

Invention Machine Corporation aims at providing big companies with a tool that allows improving their innovation process using semantic analysis for document management in the heterogeneous knowledge base collected by the client. The developed system functions as a service that scans clients’ documents and creates a semantic index, which will be further used for solutions search using Subject Action Object pattern and Boolean queries. It is the syntax part that will ensure product success in the competitive search market. As in addition, the service allows searching for new knowledge in some particular domain by means of notification about new results for the specific period.

Web portal serves as the environment the client can work in. The client can make use of semantic search within the registered scientific effects, patents, and other widely used web services. The client can also index the documents on his own computer by means of a special IE plug-in. This plug-in allows simultaneous search not only on the client’s computer but also in the patents database on a special server.

SOLUTION

Goldfire Innovator is a combination of several products developed by Invention Machine for several years. Therefore, Goldfire Innovator includes many modules developed from the already existing solutions that were updated according to the market requirements.

Analysis & planning phase

The basic product to be modified was the CoBrain web portal that allows search but without semantic analysis. The marketing department in Boston conducted research and analysis to determine what new features and tools the new service should have.

The list of features was sent to technical specialists who created a preliminary project plan with preliminary schedules and a list of deliverables. The project turned out to consist of two parts – semantic search service and user portal. The project was realized by the data science team in Minsk (server, GUI, and QA teams were set up), management and marketing were conducted in Boston (USA). ScienceSoft’s QA specialists set up a customized development process compatible with ISO 9001.

Design phase

The development team started to define the customer’s requirements. A detailed project plan was created with a thorough risk analysis to minimize their possible impact. GUI team created vision documents, interface templates, etc. After approval of the templates by the Invention Machine management, a detailed plan for GUI creation was elaborated. A set of deliverables was defined:

• At the first stage, newly implemented and updated CoBrain features had to be delivered.
• At the second stage, semantic search features were to be integrated.
• At the third stage, plug-in functionality for the client machine had to be realized.

Implementation phase

During the implementation phase, the Service and GUI teams worked in close cooperation to determine input and output data format. As a result, the service and GUI parts were developed in parallel. The service team provided special patches for the GUI team and the GUI team always had the required functionality to prepare the user interface for it.

GUI features were also developed in parallel. Moreover, the GUI team used CFML-Сustom-Tag widely. This allowed the team to create a web component library that allowed to significantly minimize UI development and testing time.

Source codes of both GUI modules and Service modules were stored in a centralized SourceSafe base and were available to all specialists participating in the development process. Therefore, not only storage procedure but also an accounting of efforts to implement specific features was carried out.

All project modules passed through the phase of preliminary testing. Unit tests were created for the Service modules, and for GUI models, in its turn, particular System tags were tested in accordance with the described interface.

All the tasks performed by developers were registered in the defect tracking system – TrackGear. Each developer provided a report on the performed work and specified the changed codes. After the Service and GUI components of each particular feature were ready, the feature was marked as implemented and ready for integration.

Integration phase

After some feature was ready for testing it was integrated into a web portal.

Stabilization phase

Testing was performed the next day after a feature was integrated into the portal. QA group tested the feature and registered bugs in Track Gear. The defect was discussed with developers and the code was fixed. If any new requests appeared they could be registered in the bug tracking system as change requests. After all, defects were fixed, the feature was considered to be implemented.

Beta versions of the product were step by step uploaded to Invention Machine’s website. The Customer’s employees and CoBrain users performed beta testing. And after implementing change requests, the product was ready for public use.

Deployment phase

The final version of the product became available on Invention Machine’s website. The product functionality was thoroughly documented. Each module had a detailed description. That allowed the team to easily proceed to the support stage. The final version was uploaded to the IM website for public use. The users provided their feedback in the system and submitted change requests that were implemented in further product versions.

Maintenance & support phase

When the warranty period expired, the Customer and ScienceSoft signed the Service Level Agreement that specified Maintenance & Support implementation. Work on the project was divided into 2 parallel processes:

• Defects reporting, tracking, fixing; minor change requests implementation (Support).
• Major change requests accumulation, planning, and implementation (Maintenance).

RESULTS

Goldfire Innovator allows a structured approach to inventive problem-solving. It helps users to easily identify the problem, generate solutions and solve the problem with the highest efficiency.

Goldfire Innovator delivers crucial patent and scientific content – including access to:

• 15 million worldwide patents,
• Database of 8000 scientific effects, 3
• 3000 cross-disciplinary scientific ‘deep web’ websites

The combination of inventive software and rich content enables organizations to bring greater efficiency to their innovation and problem-solving processes by empowering engineers with methodologies, disciplines, and relevant knowledge.

The developed solution:

• Ensures better problem definition and understanding;
• Automates and facilitates the processes around concept creation;
• Enables detailed value analysis of existing physical devices and production processes;
• Defines and prioritizes engineering problems and solutions;
• Facilitates capturing and sharing of corporate and personal knowledge by eliminating reinvention and promoting engineering reuse;
• Facilitates competitive analysis, patent analysis, and technology trend analysis;
• Infuses better market knowledge earlier in the product development process.

Whether conceiving new products, correcting product defects, designing feature modifications to existing products, identifying technology trends and future product roadmaps, or improving production processes, Goldfire Innovator enhances and accelerates the ability of engineering, marketing, and production personnel to methodically explore and validate more cost-effective, competitive, and higher-quality system designs.

Companies using Goldfire Innovator benefit from:

• Improved quality and rate of idea generation;
• Greater conversion rates of ideas to products;
• Better and more competitive product offerings;
• Streamlined manufacturing processes;
• Faster time-to-market;
• Greater R&D return on investment;
• Accelerated corporate growth.

TECHNOLOGIES AND TOOLS

Technologies & tools: Microsoft Visual C++, Java, Macromedia ColdFusion 5.0, JavaScript, Oracle 9i Database Server, IBM DB2, MAPI, ISAPI Filters, TCP/IP, HTTP, WinHTTP, IIS 4.5.

Language: C++, HTML/DHTML, XML, WDDX, JavaScript, CFML

Development processes: ISO9001

CUSTOMER

The Customer is an African-based telecom company participating in the federal Lifeline Support Program and providing pre-paid cell phones and service packages to low-income individuals.

CHALLENGE

As a part of the project, SDProtechologies’s analytics team was to design and implement a data management and analytics platform to let the Customer collect the data from multiple sources and get insights into customer behavior. The Customer wanted the platform to analyze historical data and enable forecasting. Access rights were another issue to solve, as the Customer planned to provide their tenants with access to the tenant-related analytics.

SOLUTION

The data analytics platform was gathering raw data (such as user’s impressions and click-throughs, tariff plans, device models, apps installed, and more) from 10+ sources. To collect this telemetry data and move it into Apache Kafka, ScienceSoft’s big data team suggested the MQTT protocol.

The team also suggested using Amazon Spot Instances to reduce the costs of AWS computing resources. To ensure the analytical system’s scalability, they used AWS Application Load Balancers.

Apache Kafka acted as a data streaming platform. There, the raw data was organized for further offload into the landing zone that was running on Amazon Simple Storage Service. For data storage and warehousing, Amazon Redshift was chosen, where the telemetry data from mobile phones running on Android, as well as the information from the Enterprise Resource Planning and the Home Location Register (HLR) was supplied to.

To enable regular and ad-hoc reporting, ScienceSoft developed ROLAP cubes with 30+ dimensions and 10+ facts. For instance, the analytical system measured advertising impressions and click-throughs of a particular user to calculate the reward points earned. Another example: based on the increased number of calls to the support, the Customer could expect that the user was likely to be dissatisfied with the service. With no measures taken, that could lead to customer churn.

Not only the Customer but also their tenants (also telecom companies with their own customers and HLRs) were granted access to the platform for valuable insights. For instance, a tenant can access the part of analytics related to their company. To make this possible, ScienceSoft’s team introduced two approaches: shared access (organized at the data warehouse level) and dedicated access (involving a separate AWS account).

RESULTS

With SDProtechnologies big data services, the Customer was able to:

• Measure the engagement and identify the preferences of a particular user.
• Spot trends in the users’ behavior.
• Make predictions about how users would behave.
• Invoice advertisers based on their calculated share.
• Benefit from insightful data analytics (for example, daily earnings, number of new users, customer service data, and more).

The use of Amazon Spot Instances allowed the Customer to reduce the costs of AWS computing resources by 80%.

TECHNOLOGIES AND TOOLS

Amazon Web Services (Amazon cloud), Apache Kafka (data streaming), the Message Queuing Telemetry Transport Protocol, Amazon Simple Storage Service (persistent storage used for data landing zone), Amazon Redshift (data warehouse), Airbnb Airflow, and Python (ETL).