Cognitive and Contextual Enterprise Mobile Computing

Cognitive and Contextual Enterprise Mobile Computing

 

Gabi Zodik

MobileFirst Global Research Leader

IBM Research

Haifa, Israel

zodik@il.ibm.com

 

Abstract

The second wave of change presented by the age of mobility, wearables, and IoT focuses on how organizations and enterprises, from a wide variety of commercial areas and industries, will use and leverage the new technologies available. Businesses and industries that don’t change with the times will simply cease to exist.

Applications need to be powered by cognitive and contextual technologies to support real-time proactive decisions. These decisions will be based on the mobile context of a specific user or group of users, incorporating location, time of day, current user task, and more. Driven by the huge amounts of data produced by mobile and wearables devices, and influenced by privacy concerns, the next wave in computing will need to exploit data and computing at the edge of the network. Future mobile apps will have to be cognitive to ‘understand’ user intentions based on all the available interactions and unstructured data.

Mobile applications are becoming increasingly ubiquitous, going beyond what end users can easily comprehend. Essentially, for both business-to-client (B2C) and business-to-business (B2B) apps, only about 30% of the development efforts appear in the interface of the mobile app. For example, areas such as the collaborative nature of the software or the shortened development cycle and time-to-market are not apparent to end users. The other 70% of the effort invested is dedicated to integrating the applications with back-office systems and developing those aspects of the application that operate behind the scenes.

An important, yet often complex, part of the solution and mobile app takes place far from the public eye-in the back-office environment. It is there that various aspects of customer relationship management must be addressed: tracking usage data, pushing out messaging as needed, distributing apps to employees within the enterprise, and handling the wide variety of operational and management tasks-often involving the collection and monitoring of data from sensors and wearable devices. All this must be carried out while addressing security concerns that range from verifying user identities, to data protection, to blocking attempted breaches of the organization, and activation of malicious code. Of course, these tasks must be augmented by a systematic approach and vigilant maintenance of user privacy.

The first wave of the mobile revolution focused on development platforms, run-time platforms, deployment, activation, and management tools for multi-platform environments, including comprehensive mobile device management (MDM). To realize the full potential of this revolution, we must capitalize on information about the context within which mobile devices are used. With both employees and customers, this context could be a simple piece of information such as the user location or time of use, the hour of the day, or the day of the week. The context could also be represented by more complex data, such as the amount of time used, type of activity performed, or user preferences. Further insight could include the relationship history with the user and the user’s behavior as part of that relationship, as well as a long list of variables to be considered in various scenarios. Today, with the new wave of wearables, the definition of context is being further extended to include environmental factors such as temperature, weather, or pollution, as well as personal factors such as heart rate, movement, or even clothing worn.

In both B2E and B2C situations, a context-dependent approach, based on the appropriate context for each specific user, offers a superior tool for working with both employees and clients alike. This mode of operation does not start and end with the individual user. Rather, it takes into account the people surrounding the user, the events taking place nearby, appliances or equipment activated, the user’s daily schedule, as well as other, more general information, such as the environment and weather.

Developing enterprise-wide, context-dependent, mobile solutions is still a complex challenge. A system of real added-value services must be developed, as well as a comprehensive architecture. These four-tier architectures comprise end-user devices like wearables and smartphones, connected to systems of engagement (SoEs), and systems of record (SoRs). All this is needed to enable data analytics and collection in the context where it is created. The data collected will allow further interaction with employees or customers, analytics, and follow-up actions based on the results of that analysis. We also need to ensure end-to-end (E2E) security across these four tiers, and to keep the data and application contexts in sync. These are just some of the challenges being addressed by IBM Research.

As an example, these technologies could be deployed in the retail space, especially in brick-and-mortar stores. Identifying a customer entering a store, detecting her location among the aisles, and cross-referencing that data with the customer’s transaction history, could lead to special offers tailor-made for that specific customer or suggestions relevant to her purchasing process. This technology enables real-world implementation of metrics, analytics, and other tools familiar to us from the online realm. We can now measure visits to physical stores in the same way we measure webpage hits: analyze time spent in the store, the areas visited by the customer, and the results of those visits. In this way, we can also identify shoppers wandering around the store and understand when they are having trouble finding the product they want to purchase. We can also gain insight into the standard traffic patterns of shoppers and how they navigate a store’s floors and departments. We might even consider redesigning the store layout to take advantage of this insight to enhance sales.

In healthcare, the context can refer to insight extracted from data received from sensors on the patient, from either his mobile device or wearable technology, and information about the patient’s environment and location at that moment in time. This data can help determine if any assistance is required. For example, if a patient is discharged from the hospital for continued at-home care, doctors can continue to remotely monitor his condition via a system of sensors and analytic tools that interpret the sensor readings.

This approach can also be applied to the area of safety. Scientists at IBM Research are developing a platform that collects and analyzes data from wearable technology to protect the safety of employees working in construction, heavy industry, manufacturing, or out in the field. This solution can serve as a real-time warning system by analyzing information gathered from wearable sensors embedded in personal protective equipment, such as smart safety helmets

and protective vests, and in the workers’ individual smart-phones. These sensors can continuously monitor a worker’s pulse rate, movements, body temperature, and hydration level, as well as environmental factors such as noise level, and other parameters. The system can provide immediate alerts to the worker about any dangers in the work environment to prevent possible injury. It can also be used to prevent accidents before they happen or detect accidents once they occur. For example, with sophisticated algorithms, we can detect if a worker falls based on a sudden difference in elevations detected by an accelerometer, and then send an alert to notify her peers and supervisor or call for help. Monitoring can also help ensure safety in areas where continuous exposure to heat or dangerous materials must be limited based on regulated time periods.

Mobile technologies can also help manage events with massive numbers of participants, such as professional soccer games, music festivals, and even large-scale public demonstrations, by sending alerts concerning long and growing lines or specific high-traffic areas. These technologies can be used to detect accidents typical of large-scale gatherings, send warnings about overcrowding, and alert the event organizers. In the same way, they can alleviate parking problems or guide public transportation operators- all via analysis and predictive analytics.

IBM Research – Haifa is currently involved in multiple activities as part of IBM’s MobileFirst initiative. Haifa researchers have a special expertise in time- and location-based intelligent applications, including visual maps that display activity contexts and predictive analytics systems for mobile data and users. In another area, IBM researchers in Haifa are developing new cognitive services driven from the unique data available on mobile and wearable devices.

Looking to the future, the IBM Research team is further advancing the integration of wearable technology, augmented reality systems, and biometric tools for mobile user identity validation.

Managing contextual data and analyzing the interaction between the different kinds of data presents fascinating challenges for the development of next-generation programming. For example, we need to rethink when and where data processing and computations should occur: Is it best to leave them at the user-device level, or perhaps they should be moved to the back-office systems, servers, and/or the cloud infrastructures with which the user device is connected?

New-age applications are becoming more and more distributed. They operate on a wide range of devices, such as wearable technologies, use a variety of sensors, and depend on cloud-based systems.

As a result, a new distributed programming paradigm is emerging to meet the needs of these use-cases and real-time scenarios. This paradigm needs to deal with massive amounts of devices, sensors, and data in business systems, and must be able to shift computation from the cloud to the edge, based on context in close to real-time. By processing data at the edge of the network, close to where the interactions and processing are happening, we can help reduce latency and offer new opportunities for improved privacy and security.

Despite all these interactions, data collection, and the analytic insights based upon them-we cannot forget the issues of privacy. Without a proper and reliable solution that offers more control over what personal data is shared and how it is used, people will refrain from sharing information. Such sharing is necessary for developing and understanding the context in which people are carrying out various actions, and to offer them tools and services to enhance their actions.

In the not-so-distant future, we anticipate the appearance of ad-hoc networks for wearable technology systems that will interact with one another to further expand the scope and value of available context-dependent data.

Reference:

Gabi Zodik. Cognitive and Contextual Enterprise Mobile Computing [R]. ISEC ’16, 2016. DOI: http://dx.doi.org/10.1145/2856636.2876471

Comments are closed