Sharad Mehrotra - US grants

This research focuses on developing an integrated multimedia information retrieval and database management infrastructure that provides support for multimedia information as first-class objects capable of being stored and retrieved based on their rich internal content. Specifically, techniques are being explored to (1) extract and represent multimedia content at multiple semantic levels useful for retrieval; (2) generalize text-based information retrieval approaches for content-based multimedia retrieval; (3) map high-level queries to lower-level representations supported by the database; (4) perform feature indexing that overcomes the high-dimensionality and non-euclidean nature of multimedia feature spaces; (5) integrate feature indexing mechanisms as access paths in database management systems; and (6) support ranked retrieval in database management systems. Based on the research, a prototype system, entitled Multimedia Analysis and Retrieval System (MARS), that seamlessly integrates multimedia data with structured information traditionally stored in databases is being designed. On the educational front, the existing database curriculum is being modified to incorporate evolving research and development in multimedia information systems. Also, the MARS prototype system is being made available as a teaching, education, and research tool to study human factors issues in supporting content-based retrieval. This research not only has the potential to impact the design of future multimedia information systems but will also prove beneficial to other areas including geographical information retrieval, planning and reasoning applications over database systems. http://www-mars.cs.uiuc.edu

With the advances in embedded processors, low cost sensor technologies, and wireless communication, unprecedented amounts of diverse types of information about the real world and its activities are being generated. Much of the information is spatio-temporal in nature; concerning objects dispersed in space and time, and interacting and communicating with each other and their surroundings. An infrastructure that facilitates real-time capture, storage, processing, display, and analysis of the information generated will truly revolutionize a wide variety of application domains. Examples of domains that will benefit from this technology include avionics, ground traffic, commercial applications such as ship-ping and transportation, emergency response and disaster relief operations, physical phenomenon such as weather and storm tracking, forest fire tracking, migration patterns of animals/birds, command and control, smart environments, etc. Applications in the above domains require real-time monitoring, tracking and analysis of objects/events/phenomena in space and time.

An integral component of such sensor enriched communication and information infrastructure is a database management technology that allows seamless access to information dispersed across a hierarchy of storage, communication and processing units - from sensor devices, where data originates, to large data banks where the information generated is stored for analysis and mining. This research will explore next generation database management system technology that provides effective support for information processing in highly distributed and dynamic sensor-enriched environments. The approach taken will be end-to-end - that is, research will be conducted on all aspects of the system ranging from representation, data modeling, query languages, data structures, query optimization, query processing, distribution, and concurrent accesses. A prototype database management infrastructure that supports highly dynamic geographically dispersed spatio-temporal data, multi-resolution representation of data, and provides effective support for visualization and analysis will be developed.

The goal of this research project is to improve our understanding of how humans behave in information-seeking digital environments such as the Web. The approach consists of using massively large Web logs to infer patterns of behavior. New probabilistic models for modeling human behavior on the Web are under investigation including Markov and switching models, mixture models, and Bayesian hierarchical models. Adaptive statistical techniques form the basis for building up individual user models in an online fashion, automatically learning both the dynamic time-dependent patterns of a user as well as text-vector representations of their interests. Test data sets from large commercial Web sites are being used to develop, validate, and test the models. Data are anonymized to protect individual privacy. The statistical user models are in turn being used to develop two primary software tools. The first tool allows an analyst to explore, cluster, predict, and visualize Web logs with millions of entries, allowing an understanding of dynamic patterns of access and behavior in a manner that is not currently available in research or commercial tools. The second tool, WebMARS, uses adaptive user-models to enhance information retrieval algorithms by interpreting search queries in a personalized manner. More generally, the results from this project will provide tools and techniques to enable a better scientific understanding of modes of human behavior across a broad range of digital environments, with potential applications in wireless information appliances, medical informatics, scientific exploration of massive data sets, and so forth.

Rapid advances in networking and Internet technologies has fueled the emergence of the "software as a service" model for enterprise computing that enables organizations to outsource many Information Technology (IT) services. This model allows organizations to concentrate on their core business instead of sustaining large investments in IT. IT outsourcing results in savings from the economies of scale due to leveraging of hardware, software, personnel, as well as maintenance and upgrade costs. Outsourcing is a common practice in Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) domains and it is gaining popularity in basic services such as email, storage and disaster protection.

This research will explore the data privacy challenges that arise in outsourcing data management services. Data management systems are among the most common, expensive, and complex software systems used by almost all types of organizations. In the envisioned "database as a service" (DAS) model, the client's data resides on the premises of the service provider and is accessed using SQL queries. Since clients' data as a very valuable asset, the service provider must implement sufficient security measures to guarantee data privacy. The research will explore the resulting challenges: (1) Privacy protection from malicious outsiders: protecting service providers from theft of customer data (e.g., hackers breaking into a provider's site and scanning all disks). (2) Privacy protection from database service providers: assuring that clients' encrypted data cannot be decrypted at the service provider. Thus, techniques to evaluate queries over encrypted data at the service provider need to be developed. (3) Ensuring Integrity of the Results: developing scalable techniques to ensure that the service provider returns exactly the right answer set to the client's query.

The long-term goals of this project are to radically transform the ability of organizations that respond to man-made and natural disasters to gather, process, manage, use and disseminate information both within the emergency response agencies and to the general public. The project explores a multidisciplinary approach consisting of two interrelated research thrusts:
- Scalable and robust information technology solutions to facilitate access to the right information, by the right individuals and organizations, at the right time, and
- Social science research that investigates the distinctive nature of dynamic virtual organizations, and the social and cultural aspects of information sharing across organizations and individuals.

Research challenges addressed include mechanisms to: enable crisis responders to become rich sources of vital situational information; seamlessly collect data from heterogeneous sources; translate low-level noisy data into meaningful information that can be effectively used for damage assessment and situation awareness; facilitate information sharing and collective decision-making across emergent virtual organizations; and rapidly disseminate information in the form most useful to recipients. Close collaborations with multiple government agencies have been developed to test and validate research in live environments.

The project is expected to result in robust information systems that enable first responders to make well-informed and better decisions, to prioritize their response, and to focus on activities that have the highest potential to save lives and property. The resulting timely and effective response can contain or prevent secondary disasters, and reduce the resulting economic losses and social disruption during disasters.

The project will create new shared data sets for text, video and data mining. This will allow a larger scientific community to test algorithmic innovations against these field gathered data sets. Our community outreach programs will help generate greater awareness of the role of IT, stimulating new innovations as first responders interact more closely with researchers. Our educational programs will generate a better trained crisis management work force.

Broader Impact: Timely and effective response to natural or man-made disasters can reduce deaths and injuries, contain or prevent secondary disasters, and reduce the resulting economic losses and social disruption. During a crisis, responding organizations confront grave uncertainties in making critical decisions. There is a strong correlation between the quality of these decisions and the accuracy, timeliness, and reliability of the situational information (e.g., state of the civil, transportation and information infrastructures) and the availability of resources (e.g., medical facilities, rescue and law enforcement units) to the decision-makers. Recently, at UCI and UCSD many projects have been launched that address the technological challenges in with the objective of radically transforming the ability of organizations to gather, manage, use and disseminate information when responding to man-made and natural catastrophes. Dramatic improvements in the speed and accuracy at which information about the crisis flows through the disaster response networks has the potential to revolutionize crisis response saving human lives and property. . The purpose of this infrastructure proposal is to establish an campus-level experimental Information Technology infrastructure, called Responsphere, to serve as a platform for development, testing, and validation of our current research efforts on responding to a crisis.
Intellectual Merit: Challenges in bringing accurate, timely, and relevant information to decision-makers during crisis response arises due to the scale and complexity of the problem, the diversity of data and data sources, the state of the communication and information infrastructures through which the information flows, and the unique character and dynamic nature of the responding organizations. To address these challenges, our research team is exploring a multidisciplinary approach focusing on the following research elements in the context of crisis response: (1) Enabling humans (rescue workers, observers) to become rich sources of vital crisis-related information; (2) Seamlessly collecting data from heterogeneous sources in highly dynamic disaster situations where the IT infrastructure may have partially failed; (3) Translating low-level noisy data into meaningful events useful for damage assessment and situation awareness; (4) Enabling information sharing and collective decision-making across highly dynamic emergent virtual organizations; (5) Rapidly disseminating information in the form most useful to recipients while observing the fundamental limitations of the underlying communication and information technologies. Validation platforms and testbeds will be deployed in close partnership with first responders from the City and County of LA, San Diego and Irvine Police departments as well as the California Office of Emergency Services in live environments and will help us evaluate the effectiveness of the research.

The testbeds created as a part of Responsphere will provide the team with an experimental platform to field-test and refine research on information collection, analysis, sharing, and dissemination in controlled yet realistic settings significantly enhancing their research capability. Specific testbeds include:
Mobile Incidence Level Response (MILLR) Testbed
Crisis Assessment, Mitigation, and Analysis (CAMAS) Testbed
Advanced Traffic Rerouting for Unplanned Events (TRUE) Testbed
At UC Irvine we will (1) expand the campus 802.11b based wireless infrastructure to cover major outdoor regions, (2) add instrumentation and management tools to the campus wireless environment, (3) add compute, visualization and storage capabilities for crisis management and response research, and (4) expand the available pool of mobile devices and embellish them with specialized video capture and streaming capabilities suitable for field response experiments. At UC San Diego we will (1) establish RF propagation modeling capabilities using GIS and 3D data generation and transformation tools, (2) build a wireless communications infrastructure in the Gas Lamp Quarter downtown, (3) build a command and control prototyping environment in a visualization facility, (4) create a vehicular based mobile command and control platform, (5) design a location/tracking system and prototype it along with other sensing and communications equipment in a custom man worn implementation ("manpack"), and (6) participate in the upgrade of the UCSD Police communication environment.. Specific research components tested in Responsphere are (1) a system for accurate position location in uncertain environments; (2) an integrated end-to-end quality aware distributed data collection system; (3) an end-to-end data analysis system; and (4) system for seamless multimodal interaction involving audio/video and image information. Other research efforts at UCI and UCSD in the areas of mobile computing, networking, middleware, security and ubiquitous computing will benefit from the Responsphere infrastructure

In crisis response domains, emotional manifestations are very complex and extreme emotions are common. While speech technologies have shown significant progress over the years, recognizing and understanding emotional speech in noisy environments is still a big challenge. Understanding such language is daunting given fragmented and ungrammatical utterances in addition to errors
from automatic speech recognition (ASR). Furthermore, there is little
research on the analysis of the relationship between emotion detection and language understanding which have traditionally been viewed as parallel independent tasks. Even when the output of one of these tasks is used as an input feature to the other, typically, during training a "true" classification is used instead of the "estimated" classes (as would be the case if the system were to be used in a real-setting) resulting in a mismatch and degraded performance. This project attempts to overcome such a limitation of current approaches by focusing on analyzing the degree of the dependencies between emotion and intent and investigating joint classification methods via multitask learning for language understanding and emotion detection tasks.


The primary intellectual merit of the project is integrated research on
developing an end-to-end information processing system that has the potential to very significantly impact the crisis response process. For speech processing, communucation between individuals and emergency dispatch personnel
as well as communications during responders during response pose a big challenge since callers are typically very emotional and the language used reflects that. Processing such speech requires significant research, and this project can is a first step toward this genre.

The goal of this project is to develop a semantic foundation, cross-layer system architecture and adaptation services to improve dependability in instrumented cyberphysical spaces (ICPS) based on the principles of "computation reflection". ICPSs integrate a variety of sensing devices to create a digital representation of the evolving physical world and its processes for use by applications such as critical infrastructure monitoring, surveillance and incident-site emergency response. This requires the underlying systems to be dependable despite disruptions caused by failures in sensing, communications, and computation. The digital state representation guides a range of adaptations at different layers of the ICPS (i.e. networking, sensing, applications, cross-layer) to achieve end-to-end dependability at both the infrastructure and information levels. Examples of techniques explored include mechanisms for reliable information delivery over multi-networks, quality aware data collection, semantic sensing and reconfiguration using overlapping capabilities of heterogeneous sensors. Such adaptations are driven by a formal-methods based runtime analysis of system components, resource availability and application dependability needs. Responsphere, a real-world ICPS infrastructure on the University of California at Irvine campus, will serve as a testbed for development and validation of the overall ?reflective? approach and the cross-layer adaptation techniques to achieve dependability. Students at different levels (graduate, undergraduate, K-12) will be given opportunities to gain experience with using and designing real-world applications in the Responsphere ICPS via courses, independent study projects and demonstration sessions. Students will benefit tremendously from exposure to new software development paradigms for the ICPSs that will be a part of the future living environments.

This proposal explores effective solutions to RAM scraping attacks in
the context of data management workloads. Recent studies identify
RAM scraping attacks, wherein an adversary installs malware to steal
memory resident data, as one of the major causes of data thefts in
enterprise information systems. Proposed research explores
new vulnerabilities introduced by RAM scraping and develops
practical solutions that offer the right blend of security against such
attacks while keeping performance degradation (due to cost/overhead of
security scheme) within acceptable limits. The research team will explore
realistic attack scenarios, model adversarial capabilities and constraints
for typical DBMSs (e.g., small duration repeated attacks, effects of
buffering and data lifetime, presence of concurrent queries, bandwidth limits,
etc.), design criteria of a risk-aware query optimization that can account
for a variety of disclosure risks emanating from RAM-scraping malwares;
and develop optimization techniques that simultaneously optimize performance
and reduce disclosure risks. Proposed research also explores the threat of
RAM-scraping attacks for the emerging cloud-based data processing frameworks
like MapReduce.


Intellectual Merit: The proposed research represents the first attempt
towards a new direction of research , viz. mitigating impact of RAM
scraping attacks in the context of DBMS workloads, that has not been
addressed by the database research community at large. The proposed
approach of redesigning a query optimizer to explore tradeoffs between
performance and disclosure risks represents a significant innovation in
query processing and memory management in the database management area.
Furthermore, exploration of RAM scraping attacks in MapReduce style
cloud-based data processing frameworks represents new innovation in cloud
security.

Broader Impact: The results of this EAGER grant will help launch a new line
of research within database security community ? viz., risk aware database
management that balances data exposure risks from RAM scraping attacks with
performance degradation in a variety of adversarial settings. The proposed
research could also make a significant impact on the DBMS vendors and data
service providers in the emerging cloud computing framework.

For further information see the project web site at the URL:
http://www.ics.uci.edu/~projects/privacygroup/projects.html

During database query processing, data migrates across different system components each of which may offer different levels of security guarantees and may be susceptible to different attacks. If the underlying data (or part of it) is sensitive, data migration, especially from secure components to those that are relatively insecure could increase risk of data loss. This proposal takes a "risk-based" approach to security wherein instead of designing approaches to prevent attacks, the proposed research controls flow of data during query processing through various components in such a way to strike a balance between risk of exposure and system performance. Risk-aware query processing techniques is explored in two settings: (a) A stand-alone database server where data on disks is stored encrypted (and is hence secure) but is loaded in plaintext into memory during query processing. (b) Cloud computing environment where, during peak load queries (and corresponding data) are shipped from (relatively secure) private storage to be processed at (relatively insecure) public cloud infrastructure, a phenomena known as cloudbursting. In both these settings, techniques to co-optimize query execution to simultaneously minimize both disclosure risks as well as performance costs are explored. The research offers a complementary approach to traditional techniques based on preventing attacks to support practical security in the context of database systems.

The project seeks to help launch a new direction to database security research that explores techniques to limit risks (instead of only preventing attacks). The research, if successful, will make cloud-based data management solutions more secure increasing the rate of their adoption even for applications that have substantial data confidentiality concerns. Finally, the planned research, system development, and educational activities are expected to significantly enhance the educational experience of students.

Dr. Sharad Mehrotra and an interdisciplinary team of collaborators at the University of California at Irvine (UCI) will develop I-sensorium, to serve as a "living laboratory" to support research in several related areas of cyber-physical systems: including theoretical foundations and underlying principles of building sentient systems; engineering, software, and systems level challenges; and novel application contexts where such sentient systems can be used. I-Sensorium will serve as a testbed for exploring and testing novel sentient technologies. I-Sensorium augments Responsphere, an existing UCI crisis response test-bed with state-of-the-art storage and computational infrastructure to acquire and process continuous streams of sensory data from Responsphere sensors. The I-Sensorium software will leverage multiple ongoing research projects at UCI on large-scale, heterogeneous sensor databases, sensor middleware platforms for querying and analysis of heterogeneous sensor data, and an event representation system for multi-media data, to provide a high level programming environment for the I-Sensorium allowing a broad group of researchers to participate and benefit from its creation.

The development of I-Sensorium offers opportunities for research-based education and training in many aspects of cyber-physical systems. The infrastructure as well as the sensor data acquisition enabled by it presents research challenges and opportunities in sensor data management, machine learning, computer vision, event modeling, among others. Interactions with industrial partners and government agencies are expected to lead to advances in the applications of cyber-physical systems in real-world settings. Education and outreach efforts are aimed at broadening the participation of women and members of underrepresented groups in computer science and engineering. Further information about this project can be found at: http://www.i-sensorium.org

Data cleaning technologies, traditionally designed to improve quality of data in back-end data warehouses, are fast emerging as a vital component of real-time information access. As the Web evolves towards supporting interactive analytics and basic search migrates from simple keyword retrieval to retrieval based on semantically richer concepts (e.g., entities) extracted from web pages, the need for "on-the-fly" cleaning techniques that can help alleviate data quality challenges is rapidly increasing. This project explores three new innovations that will help advance data cleaning towards becoming an embedded enabling technology for real-time information access. The first innovation is "query-aware data cleaning" which is based on the observation that the specificity of the real-time task such as a query can be exploited significantly to bring new optimizations to the data cleaning process. The second innovation is a data cleaning framework that migrates from the "best-effort" adhoc setup of today's systems into a principled approach that exposes and exploits a fundamental tradeoff between the cost of cleaning and quality of results achieved. Finally, since results of cleaning need to be fed to the end-user or analysis code, the proposal postulates and addresses approaches towards how results processed through data cleaning code can be presented to the end-recipient. The primary contribution is mechanisms to hide the uncertainty in the data and determinize the results while maximizing the end application goals.

The proposed research is intended to bring transformative improvements in interactive analytics and search on the web by facilitating real-time data cleaning and data quality enhancements. The project also aims to benefit the research community by incorporating mechanisms developed as part of this research into the Web People Search Technology (WEST), enabling WEST to become a real-time on-the-fly web people search tool. The goal is to support WEST as a plug-and-play system wherein other researchers could embed and test their data cleaning algorithms and tools. Finally, the planned research, system development, and educational activities are going to significantly enhance the educational experience of students, preparing them for a brighter future in the today's knowledge driven society.

For further information see the project web site at the URL: http://sherlock.ics.uci.edu

Pervasive computing, such as sensors in smartphones, buildings, automobiles and cities, result in increased sharing of sensor data, whether initiated by users or by other authorities such as service providers, government entities, interest groups, and individuals. Embedded in this data is information which others, even using sophisticated data mining algorithms, can fuse to construct a virtual biography of our activities, revealing private behaviors and lifestyle patterns. Researchers in this project are devising computational methods to let users exercise privacy control over their personal sensory data that is shared.

Intellectual Merit: The project is developing a user-configurable cryptographically-secure ?privacy shield? to run on smartphones and act upon sensor information flowing to other users, apps, and services. To make privacy understandable, the user is presented with a higher level abstraction for expressing privacy and sharing in terms of rich inferences and contexts drawn from sensor measurements. The user can designate some inferences and contexts as private. To provide privacy while ensuring the quality of service provided by the recipients of the sensory information, the system also incorporates algorithms which, over time, learn a personalized model of the privacy risk from sharing an inference. The theoretical concepts and the system realization are being validated via user studies in mobile health and personal sensing.

Broader Impacts: By providing better understanding of the behavioral privacy problem and risks inherent in sharing seemingly innocuous data, results from this project will lead to a more educated and informed citizenry, regulators, and policy makers, and provide effective tools for privacy management to those who share sensory information.

SCALE2 explores the design of resilient, inexpensive cyber-physical systems (CPS) technologies to create community-wide smartspaces for public/personal safety. SCALE2 aims to demonstrate that community safety can be realized by augmenting CPS technologies with end-to-end resilience mechanisms. Such a study requires real-world community-scale deployments to understand citizen concerns and can only be achieved through partnerships between various stakeholders - researchers, government agencies, and industry.

The SCALE2 multisensory platform will use inexpensive Internet of things (IoT) components, and support dependable operation by enabling resilient information-flow through multiple system layers. Research will explore mechanisms for (a) ingest of real-time data through flexible rich data models, (b) Quality of Service (QoS)-aware messaging to cloud platforms, and (c) reliable detection of higher-level community events through semantics-driven virtual sensing. SCALE2, through its established partnerships/testbeds, offers a unique short-term opportunity to guide future resilience technologies, train the next generation of students and have broader community impact. SCALE2 will be deployed at Montgomery County, MD, and the Irvine-Sensorium working with local agencies.

This project will explore the challenge of cleaning data in the context of analysis pipelines over big data. Data cleaning has traditionally been designed to improve data quality in ETL systems where enterprise data is collected, prepared, staged, transformed, and loaded into a data warehouse to support offline data analysis. In the era of big data, such back-end processes are quickly giving way to interactive exploratory data analysis where analysts immerse themselves in data (possibly collected from heterogeneous data sources) in order to drive online (near-) real-time decision making. Existing systems do not scale to the volume, velocity, or the variability of the dynamically generated data (e.g., social media streams) and the offline architecture is unsuited for the online real-time nature of analysis. The market is abuzz with innovations in data transformation technologies, e.g., TriFacta allows analysts to visually manipulate data to generate complex analytical transformations and Data Tamer is exploring scalable data curation from diverse sources. Data quality (and hence data cleaning technologies) remain at the core of big-data analytics. Many popular media (as well as academic) articles have highlighted challenges such as entity linking and resolution as among the most important and immediate roadblocks for big data analytics. The key insight on which this project is based is that data cleaning to support analytics over big data is not simply a matter of scaling up known approaches to larger data sets by exploiting more hardware. While scale up is important, big data analytics in streaming, real-time, and interactive settings requires a paradigm shift in how data cleaning is performed. This project will significantly impact and change the modern practices of data cleaning and the way cleaning is integrated in the Big Data analysis pipeline and will explore broader impact through: (a) technology transfer opportunities with a relevant industrial partner whose existing products could benefit from the proposed research; and (b) open source effort in the context of the ongoing social media analytics system (SoDAS), currently under development, in which the proposed research algorithms will be integrated.

This research will explore two new innovations that will help advance data cleaning to enable Big Data analysis. The first innovation explores a progressive approach to entity resolution to support progressive analysis. The research will explore an approach where progressiveness is pervasive spanning all the phases of the cleaning process especially in scenarios when cleaning is based on complex logic possibly requiring dynamic acquisition of additional contextual information. The second innovation is the analysis-aware data cleaning that is developed for structured queries (e.g., Hive and SQL) for both one-time and continuous query scenarios that are issued on top of static and streaming data. The project will address these methodologies at the higher conceptual level as well as implement them on modern highly-parallel computing platforms and frameworks that run on a cluster of machines. The project will exploit two concrete contexts to guide the research exploration: (a) supporting analytical queries over structured web data sources such as fusion tables; and (b) online analysis of social media data. These application contexts will serve as vehicles for testing and demonstrating the research. The planned research, system development, and educational activities (e.g., curriculum changes to incorporate projects related to big data and data quality in the CS curriculum at UCI) will significantly enhance the educational experience of students, preparing them for a brighter future in the today?s knowledge driven society. More information about the project can be found at http://sherlock.ics.uci.edu.

Water is a critical resource and a lifeline service to communities worldwide; the generation, treatment, distribution and maintenance of water workflows is typically managed by local governments and water districts. Recent events such as water supply disruptions caused by Hurricane Sandy in 2012 and the looming California drought crisis clearly indicate society's dependence on critical lifeline services such as water and the far-reaching impacts that its disruption can cause. Over the years, these critical infrastructures have become more complex and often more vulnerable to failures. The ability to view water workflows as a community wide cyber-physical system (CPS) with multiple levels of observation/control and diverse players (suppliers, distributors, consumers) presents new possibilities. Designing robust water systems involves a clear understanding of the structure, components and operation of this CPS system and how community infrastructure dynamics (e.g. varying demands, small/large disruptions) impact lifeline service availabilities and how service level decisions impact infrastructure control.

The proposal emphasizes a new approach to exploring engineering systems that will result in substantial advances in the understanding of lifeline systems and approaches to make them adaptive and resilient. Building resilience into urban lifelines raises a number of monumental challenges including identifying the aspects of systems that can be observed/sensed and adapted and to developing general principles that can guide adaptation. The key idea is to develop methodologies to understand operational performance and resilience issues for real-world community water infrastructures and explore solutions to problems in cyberspace before instantiating them into a physical infrastructure. The effort includes: 1) Developing a flexible modeling framework that captures system needs at multiple levels of temporal and spatial abstraction; 2) Developing real-time algorithms supporting resilience; 3) Designing adaptations for water systems using a data-driven approach; and 4) Demonstrating the important broader impact of the research on critical water system infrastructure at the Global City Technology Challenge and the longer term impact on infrastructure for a resilient control framework.

he goal of this project is to facilitate timely retrieval of dynamic situational awareness information from field-deployed nodes by an operational center in resource-constrained uncertain environments, such as those encountered in disaster recovery or search and rescue missions. This is an important cyber physical system problem with perspectives drawn at a system and platform level, as well as at the system of systems level. Technology advances allow the deployment of field nodes capable of returning rich content (e.g., video/images) that can significantly aid rescue and recovery. However, development of techniques for acquisition, processing and extraction of the content that is relevant to the operation under resource constraints poses significant interdisciplinary challenges, which this project will address. The focus of the project will be on the fundamental science behind these tasks, facilitated by validation via both in house experimentation, and field tests orchestrated based on input from domain experts.

In order to realize the vision of this project, a set of algorithms and protocols will be developed to: (a) intelligently activate field sensors and acquire and process the data to extract semantically relevant information; (b) formulate expressive and effective queries that enable the near-real-time retrieval of relevant situational awareness information while adhering to resource constraints; and, (c) impose a network structure that facilitates cost-effective query propagation and response retrieval. The research brings together multiple sub-disciplines in computing sciences including computer vision, data mining, databases and networking, and understanding the scientific principles behind information management with compromised computation/communication resources. The project will have a significant broader impact in the delivery of effective situational awareness in applications like disaster response. The recent :World Disaster Report" states that there were more than 1 million deaths and $1.5 trillion in damage from disasters within the past decade; the research has the potential to drastically reduce these numbers. Other possible applications are law enforcement and environmental monitoring. The project will facilitate a strong inter-disciplinary education program and provide both undergraduate and graduate students experience with experimentation and prototype development. There will be a strong emphasis on engaging the broader community and partnering with programs that target under-represented students and minorities.

Emerging application domains such as sensor-driven smart spaces and social media platforms require incoming data to be appropriately enriched prior to being consumed by data analysts. Enrichment often requires the use of complex compiled code, declarative queries, and/or expensive machine learning/signal processing modules. Traditionally, enrichment is performed as an offline process prior to making the data available for analysis. The recent trend towards real-time analytics has prompted industrial and research systems to explore enrichment during online data processing. These efforts have focused on optimizing enrichment at the time of data ingestion. This project will develop a new type of data management technology, to support real-time data analytics, entitled EnrichDB, that represents a significant departure from the above ingestion-based enrichment approaches. EnrichDB is based on the premise that enriching data in its entirety at ingestion can be (a) wasteful -- since applications may not require all data to be enriched; (b) result in unacceptable latencies -- if data arrival rates are high, or (c) not be feasible -- if enrichment functions are learned and incorporated into the system at a later time after ingestion. EnrichDB will explore seamlessly integrating data enrichment through the entire data processing pipeline - from ingestion to event-based intermittent enrichment, and progressively during query processing. EnrichDB will benefit real-time data analytics in multiple domains including IoT-enabled smart spaces, text and social media analytics, cybersecurity, network surveillance, etc.

EnrichDB will address a variety of challenges that arise in enabling enrichment through different stages of the data processing pipeline. One such challenge is to explicitly represent the state of enrichment of the objects (i.e., which enrichment functions have been applied to which objects); such a state will drive additional enrichments required downstream in data processing. Another challenge is to develop mechanisms to support enrichment during query processing efficiently since query-time enrichment could result in unacceptable latencies. Techniques need to be designed to enrich data progressively while processing queries to provide answers at acceptable levels of quality and latency. Such progressive processing logic could either be layered on top of existing databases or could be incorporated natively into database engines by rethinking storage, indexing, and query processing to support enrichment. The project will explore challenges that arise for both these cases. Finally, self-driving strategies to decide which objects should be enriched to what degree at what stage of the data processing pipelines need to be designed. Such an approach would complement a strategy wherein such decisions are made by the system administrator. EnrichDB will be used in advanced data management classes and will be integrated into a campus-level smart space testbed at UCI entitled TIPPERS that supports a variety of services including real-time occupancy counts and other building usage analysis, at the UCI campus.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Contact tracing has emerged as a key mitigation strategy to prevent the spread of pandemics such as COVID-19. Recently, several efforts have been initiated to track individuals, their movements, and interactions using technologies such as Bluetooth beacons, cellular data records, and smartphone applications. Such solutions can be intrusive, potentially violate individual privacy rights and are often subject to regulations that mandate the need for opt-in policies to gather and use personal information which, as several studies have shown, limits their adoption. This project takes a novel approach to empower organizations to mitigate spread of COVID-19 at their premises by exploiting connection events between mobile devices carried by individuals and the Wi-Fi infrastructure. There are several advantages of the planned approach. First, it takes an organizational perspective and is intended to help organizations, small and large, keep employees safe and ensure safety on their premises by exploiting network data (already being generated by their network infrastructures). Second, it is decentralized, i.e., instead of empowering/trusting a small number of organizations such as mobile OS companies, it empowers organizations to assume joint responsibility to implement safety measures at their premises. Third, it offers a fully privacy-preserving solution based on computationally and informationally secure cryptography with strong security properties guaranteeing privacy of individuals, including those who might be exposed or carriers. This will prevent misuse of the data collected by any entity against the will of the individuals. Fourth, it is based on connectivity events already generated by existing Wi-Fi infrastructure and does not require users of the network to either download any application and/or give explicit permissions (which is known to limit adoption). Finally, it offers a path to implement technology not just for contact tracing but empowers organizations with awareness about effectiveness of their policies/strategies such as social distancing, disinfecting/cleaning schedules, etc.

The planned approach is built upon several innovations including (a) new algorithms for cleaning noisy Wi-Fi connectivity data to develop models of occupancy, (b) new cryptographic solutions to implement privacy-preserving data analytics and queries over encrypted Wi-Fi connectivity data (collected from mobile devices), to generate a range of information -- e.g., level of adherence to social distancing policies, flow of people in spaces, and exposure hotspots, (c) design of a range of COVID-19 relevant applications that help organizations ensure safety of individuals on their premises. Such applications include publicly accessible organizational portals/dashboards and subscription-based alerting technologies developed in concert with stakeholder (e.g., UCI campus administration). Deployment and testing of the solution will be done at campus scale, particularly focusing on how the technology can empower the university leadership to determine strategies for reopening research labs while maintaining health and safety of all involved and follow the applicable mandates of the public health authorities. It is expected that the project will serve as a vehicle to inform and educate both individuals and scientists about virus transmission and spread and will contribute to the development of processes and actions at the organizational level to mitigate spread of COVID-19.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The provided support will enable about 15 US-based students to attend and participate in the 46th International Conference on Very Large Databases (VLDB 2020)to be held in Tokyo Aug. 31st to Sept. 4th, 2020 (https://vldb2020.org/). The VLDB conferences are a premier international technical event centered on various aspects of database research and practice. The VLDB conference provides a forum that promotes students and researchers to identify scientific foundations for building large-scale database management systems that can work ever more effectively. Efforts will be made to broaden the participation of underrepresented students and researchers. The conference will thus contribute directly to training the next generation of scientists who are both consumers and developers of technology in database management system design and implementation. The involvement of US-based students will have a direct impact in creating a highly-qualified workforce who can take on the emerging data science challenges of the future.

Database research profoundly influences domains that include health sciences, the emerging field of Internet of Things (IoT), service-oriented computing, real-time business analytics, and social computing. The VLDB conference series provides researchers and practitioners from academia, industry and government agencies with an excellent opportunity to share their research and experiences on all aspects of data management and analysis at scale. The aims of the conference are multi-fold: (1) to provide an international forum for sharing research results related to the investigation of major challenges in next-generation database research; (2) to stimulate interactive discussions on research in the database area through panels, posters, tutorials, student scholar roundtables, etc.; and (3) to motivate and engage future generations of researchers via the many conference and workshop presentations, interactive poster sessions, and panel and breakout sessions. The VLDB 2020 Proceedings are will be broadly accessible at The Proceedings of the VLDB Endowment (http://www.vldb.org/pvldb/).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Smart spaces are characterized by the presence of smart services built on emerging sensing, computing, and communication technologies. This planning grant will create a multi-university Center for Smart Space Research (CSSR) involving Rochester Institute of Technology (RIT) and the University of California, Irvine (UCI). CSSR takes an integrated approach to make them versatile and adaptable to situations by ensuring the availability and accessibility of data, knowledge, and services. This planning grant will identify research challenges to build application-focused smart spaces. Applications encompass health and disease monitoring systems, smart buildings, public spaces and cities, mission-critical systems, transportation, intelligent manufacturing, collaborative robotics, and others.

The center’s focus will involve four main areas of relevance to industry, university, and society: creation of adaptive computing and communications infrastructure; trustworthy acquisition, processing, interpretation and sustainability of data and knowledge; applications that span all aspects of life and the environment; and creation of diverse and competent workforce. UCI researchers will collaborate with industrial organizations, technology developers, and users, to investigate: adaptive and resilient multiscale network communications; data-driven real-time adaptations for mission-critical applications; multi-tier and edge-cloud computing methods, IoT data management and security/privacy techniques for instrumented spaces. These collaborations will facilitate transition of research to practice in smartspaces.

The planning phase of CSSR will impact research and pedagogical activities at UCI and stimulate technology transfer to collaborating industries. Our work will emphasize smartspace applications to improve quality of life and enhance community resilience in challenging situations. CSSR will leverage ongoing and future projects to reach underprivileged populations with varying socio-economic backgrounds. UCI is a Hispanic-serving institution with a diverse student body. The planning grant workshops will promote interactions with industry and create opportunities to recruit and mentor diverse groups of students in undergraduate capstone projects, graduate courses and internships creating a future workforce in the area of smartspaces.

In the near future, access to smart spaces and smart infrastructure will impact every individual and will greatly improve quality of life. CSSR activities in fostering innovation in accessible smart spaces will play a major role in the future world. The planning phase will serve as a platform for meetings, discussions, and workshops, to identify critical research to guide the development of smart spaces. CSSR will maintain a publicly-accessible repository comprising meeting materials, workshop information, research topics and details, and publications. This repository will be maintained at a publicly accessible URL at RIT for the lifetime of the center.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Disasters disproportionately impact older adults who experience increased fatality rates; such individuals often live in age-friendly communities and senior health facilities (SHFs). During a crisis, older adults are often unable to shelter safely in place or self-evacuate due to a range of physical conditions (need for life-sustaining equipment, impaired mobility) and cognitive afflictions (e.g. dementia, Alzheimer’s). First responders assisting older adults could benefit from seamless, real-time access to critical life-saving information about the living facilities (e.g., floor plans, operational status, number of residents) and about individual residents (e.g., health conditions such as need for dialysis, oxygen, personal objects to reduce anxiety). Such information, siloed within organizational logs or held by caregivers, is inaccessible and/or unavailable at the time of response. This interdisciplinary project brings together IT, geriatrics and resilience experts with disaster-response agencies and SHF providers to create information preparedness and transform disaster resilience for older adults.

The team will design, develop and deploy CareDEX, a novel community contributed data-exchange platform, that empowers SHFs to readily assimilate, ingest, store and exchange information, both apriori and in real-time, with response agencies to care for older adults in extreme events. The CareDEX information pipeline enables SHFs to capture individual information about changing health conditions and personalized needs and share them with responders to help improve response. Information co-produced with civic partners will identify and refine resident-specific data via tools for proactive collection/update. Given the sensitive nature of personal information, e.g., health-profiles, CareDEX will incorporate policy-based information sharing mechanisms that balance needs for individual privacy with authorized information release. CareDEX’s hybrid-cloud architecture seamlessly enables data to be securely stored on-premise (at SHF) and in the cloud for remote access by responders and temporary caregivers. Relocation of older adults requires regional information (e.g. road-conditions, facility status) - CareDEX will integrate GIS tools to provide first-responders with uptodate region-level situational awareness for dynamic decision-support. The prototype CareDEX platform will be co-developed with core civic partners, e.g. Front Porch (a nation-wide senior-care provider) and deployed at a SHF in Anaheim, CA. Collaborations with local response agencies (Los Angeles, Orange County, San Bernardino, San Diego) and national entities (FEMA, Red Cross, NFPA/FPRF) will mesh needs of emergency responders with caregivers. CareDEX will be evaluated using diverse scenarios - a wildfire event triggering relocation, wildfires coupled with a pandemic, and rapid onset earthquake events with small warning times and increased uncertainty.

The CIVIC Innovation Challenge is a collaboration with Department of Energy, Department of Homeland Security Science and Technology Directorate, Federal Emergency Management Agency (FEMA), and the National Science Foundation

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The provided support will enable United States-based students to attend and participate in the 48th International Conference on Very Large Databases (VLDB 2022) to be held in Sydney, Australia from September 5, 2022 to September 9, 2022 (https://vldb.org/2022). The VLDB conferences are a premier international technical event centered on various aspects of database research and practice. The VLDB conference provides a forum that promotes students and researchers to identify scientific foundations for building large-scale database management systems that can work ever more effectively. Efforts will be made to broaden the participation of underrepresented students and researchers. The conference will thus contribute directly to training the next generation of scientists who are both consumers and developers of technology in database management system design and implementation. The involvement of United States-based students will have a direct impact in creating a highly-qualified workforce who can take on the emerging data science challenges of the future.<br/><br/>Database research profoundly influences domains that include health sciences, the emerging field of Internet of Things (IoT), service-oriented computing, real-time business analytics, and social computing. The VLDB conference series provides researchers and practitioners from academia, industry and government agencies with an excellent opportunity to share their research and experiences on all aspects of data management and analysis at scale. The aims of the conference are multi-fold: (1) to provide an international forum for sharing research results related to the investigation of major challenges in next-generation database research; (2) to stimulate interactive discussions on research in the database area through panels, posters, tutorials, student scholar roundtables, etc.; and (3) to motivate and engage future generations of researchers via the many conference and workshop presentations, interactive poster sessions, and panel and breakout sessions. The VLDB 2020 Proceedings will be broadly accessible at http://www.vldb.org/pvldb/.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.