Summary for Mobile Application

The mobile phone is probably one of the biggest inventions and revolutions of recent times. Everyone seems to own one. Such is the reach of the mobile phone. Its no wonder, it is one of the most lucrative and promising platforms for products, solutions and entertainment.

The Supernova Infotechnologies team has experience developing mobile solutions and location based services since 2007. In USA and India. Leveraging this experience and expertise we have a mobility team that focuses on developing products for the cutting edge smart phones like Android, iPhone and Blackberry. Supernova Infotechnologies had built several smart phone applications for its clients. Below are the case studies of a few among them.

Case Study 1-

A mobile store ClockTime application for a leading financial organization. It is the perfect companion application for current ClockTime subscribers to track their time, expenses, and receipts easily. Track your time on the go, track expenses, create expense sheets, and snap pictures of your receipts. Remain productive outside the office with ClockTime Mobile. ClockTime Mobile logs all your time, correctly allocating it to your projects and tasks. Plus, capture all of your expenses–whether you’re out to lunch, traveling coast-to-coast, or even completely offline.

Built from the ground up, the Android compatible mobile app integrates seamlessly with your ClockTime Web account for instantly accessible time reporting and quick and easy expense reimbursement.

Platform: Android
Challenges:

  • Very fast development cycle, to be ready in time for the company’s investor meet
  • Capture your time against your clients, projects, and tasks while on the go
  • Upload receipts from your Android device directly from your smart phone’s camera
  • Keep track of your reimbursable totals while on the road

Case Study 2 –
Daily Care is just what the doctor ordered for your health. A secure way to connect with doctors and to manage all your health data in one place.

  • Get Remote Health Monitoring – if your doctor has a Virtual Practice for Daily Care, health data that you update in the application can be monitored and responded to by the doctor
  • Store your personal health data – medications, existing health conditions and allergies, medical reports, family health history, record of procedures and vaccinations, emergency contacts – all in one place.
  • Ask a Doctor if you have a question about your health – ask a real doctor who can also view your personal health data while answering your question. It’s better than just searching online..
  • Manage Appointments – if your doctor has a Virtual Practice for Daily Care, you can manage your appointments through the app.

Platform:iPhone
Challenges:

  • Fast way of connecting with doctors to manage their health data.

Track your health readings – your health condition varies between visits to your doctor. Track your vitals like BP, blood sugar, oxygen level; frequent test readings like HbA1c, Hb, TSH, keratinize etc; fitness indicators like weight, sleep etc

Summary for Cloud Computing

Cloud Computing refers to a model of network computing where a program or application runs on a connected server or servers rather than on a local computing device such as a PC, tablet or Smartphone. Cloud Computing refers to a computing hardware machine or group of computing hardware machines commonly referred as a server or servers connected through a communication network such as the Internet, an intranet, a local area network (LAN) or wide area network (WAN). Any individual user who has permission to access the server can use the server’s processing power to run an application, store data, or perform any other computing task. Therefore, instead of using a personal computer every time to run a native application, the individual can now run the application from anywhere in the world, as the server provides the processing power to the application and the server is also connected to a network via the Internet or other connection platforms to be accessed from anywhere.

Case Study 1-

  • IEdu is a Cloud Computing technology for Education Sector for rapidly expanding global user base, disruptive innovation in educational technology and pedagogical practices. It accelerates workload straining application architecture and infrastructure deployment. It helps in performance, security and compliance requirements. It distributes instances for availability, global performance, and load balancing for service management organization for high availability.

Platform: SaaS
Results:

  • Reduced availability incidents
  • Improved user experience and performance from global deployments.
  • Reusable application architectures based on approved reference architectures.
  • Improved agility through application migration to the public cloud.
  • Increased scalability and manageability.
  • Reduced total cost of ownership.
  • Decreased time to market for new services.

Case Study 2:

Global Reserve Application Development is a cloud transaction mechanism for a global bank that improved scalability and reduced costs. To position itself as a leader in using information technology to add value, a global private wealth management and investment banking firm sought to transform multiple IT systems as part of its modernization and organizational change initiatives centered on cloud adoption. The firm had already deployed a private cloud, but needed a highly flexible processing framework for handling millions of transactions per hour. The transaction hub included three frameworks, each leveraging best of breed, open-source technologies.

Inbound Framework
The inbound framework captures the inbound transaction as the first step in the process.

Workflow Rules and Process Framework
The process framework uses workflow rules to:

  • Evaluate each incoming transaction to identify exceptions or “breaks”
  • Analyze each break situation
  • Create corrective transactions to route to front office systems

Outbound Framework
The outbound framework routes corrective transactions and receive acknowledgements.

To deliver the transaction hub, Cloud Technology Partners designed an object-oriented application using components of the Unified Modeling Language (UML). The final architecture design incorporated several well-defined cloud principles and exceeded the client’s goals for reusability of components and patterns. After validating the POC design with the client, the team worked with the firm’s offshore (India-based) development teams to implement the successful build out of the architecture and application on the firm’s private cloud. Cloud Technology Partners also trained both the firm’s onshore and offshore developers with the processes and tools necessary to manage the new framework.

Platform: Web based
Results:

  • It helped to drive value, both internally and for its customers.
  • Reduced the client’s cash reconciliation costs
  • The transaction architecture also dramatically exceeded client expectations with respect to volume thresholds.

Summary for Big Data:

Big data is a buzzword, or catch-phrase, used to describe a massive volume of both structured and unstructured data that is so large that it’s difficult to process using traditional database and software techniques. In most enterprise scenarios the data is too big or it moves too fast or it exceeds current processing capacity. Big data has the potential to help companies improve operations and make faster, more intelligent decisions.

The challenges of working with unstructured data, illustrated in the web log example, are often characterized in terms of four Vs. The four Vs are identified as:

  • Volume – Defined as the total number of bytes associated with the data. Unstructured data are estimated to account for 70-85% of the data in existence and the overall volume of data is rising.
  • Velocity – Defined as the pace at which the data are to be consumed. As volumes rise, the value of individual data points tend to more rapidly diminish over time.
  • Variety – Defined as the complexity of the data in this class. This complexity eschews traditional means of analysis.
  • Variability – Defined as the differing ways in which the data may be interpreted. Differing questions require differing interpretations.

Case Study 1:

Traverse Time is an interactive map developed for railways where New Yorkers have to click to spot in any of the city’s five barrels for an estimate of subway or train travel times. To develop this map the city was divided into 2930 hexagons, and then pulled data from open source itinerary platform. This helps to the value of savings in personal travel time, waiting and walk times, helps in reduction of overcrowding and congestion.

Platform: Captured
Results:

  • Faster way to spot the trains location.
  • Helps in saving of time.

Case Study 2:

Complete Circle view provides networks have massive amounts of patient data and metadata, which often comprise a patchwork of siloed systems and technologies. It is increasingly challenging to store the variety of structured and unstructured data required, from basic patient information and medical histories to lab results and MRI images. Further, the lack of centralization makes it challenging for healthcare professionals and patients to access the right information at the right time. Healthcare providers can create a single application that provides a complete circle view of the patient, aggregating patient, doctor, procedure and other types of information in a single data store. By liberating patient data from silos, healthcare provider networks can serve more patients in less time, reduce the potential for malpractice and improve healthcare outcomes.

Platform: Created
Results:

  • It stores complete details of patient medical history.
  • Mobile apps for doctors and nurses.
  • Electronic healthcare records.
  • Fraud detection.
  • Serves more patients in less time.

Summary for Business Intelligence/Data-Warehousing:

Business intelligence (BI) is a set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information for business purposes. BI can handle enormous amounts of unstructured data to help identify, develop and otherwise create new strategic business opportunities. BI allows for the easy interpretation of volumes of data. Identifying new opportunities and implementing an effective strategy can provide a competitive market advantage and long-term stability. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics.

In computing, a data warehouse (DW, DWH), or an enterprise data warehouse (EDW), is a system used for reporting and data analysis. Integrating data from one or more disparate sources creates a central repository of data, a data warehouse (DW). Data warehouses store current and historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons.

Different types of systems:
Data Mart:
A data mart is a simple form of a data warehouse that is focused on a single subject (or functional area), such as Sales, Finance, or Marketing. Data marts are often built and controlled by a single department within an organization. Given their single-subject focus, data marts usually draw data from only a few sources. The sources could be internal operational systems, a central data warehouse, or external data.
OLAP (On Line Analytical Processing):

Is characterized by relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems a response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques. In OLAP database there is aggregated, historical data, stored in multi-dimensional schemas (usually star schema). OLAP would have data latency typically in few hours as opposed to data mart where latency is expected to be closer to one day. OLTP (On Line Transaction Processing) also called Decision Support Systems is characterized by a large number of short on-line transactions.

Predictive Analysis:

Predictive analytics is about finding and quantifying hidden patterns in the data using complex mathematical models that can be used to predict future outcomes. Predictive analysis is different from OLAP in that OLAP is focused on historical data analysis and is reactive in nature while Predictive analysis is focused on future. These systems are also used for CRM (Customer Relationship Management)

Case Study 1-

Be Sure is a Business Intelligence/Data Warehousing for banking in the sourcing of bank office and IT services to banks in private banking and wealth management. It offers ITO services to insurance companies and other financial institutions.

Platform : Automated multi-dimensional Warehousing
Results:
Reporting

  • Costs Allocation
  • Profitability Analysis
  • Simulation

Case Study 2-
ClubHouse is a Business Intelligence/Data Warehousing for Casino is a major Casino in the Southeast with more than 100,000 square feet of playing area had a persistent, severe tobacco odor problem despite aggressive cleaning and fresh air ventilation systems.
ClubHouse conducted a trial in a 5,000 square foot cocktail lounge area that presented a particularly difficult odor problem. Using an odor remediation agent targeted at residual cigarette smoke odors and a fragrance selected by management to match the hotel amenities scent, ClubHouse completely eliminated the malodors and provided the pleasant and memorable scent chosen by Casino management.
Platform: Managed Data mashups
Results:

  • Pleasant smell.
  • Money Making.

Please get in touch with us for more information.