During disasters, ensuring that critical response resources are efficiently allocated to the most appropriate locations is crucial for minimizing adverse impacts and saving lives. To this end, we present RADAR, a data-driven platform that integrates multisource GIS feeds (e.g. USGS earthquake alerts, Cal Fire wildfire perimeters) with facility and transportation data to support proactive planning and real-time recommendations that can be used by Emergency Operations Centers to guide populations to safety. RADAR uses policy-driven stable matching to optimize routing and resource assignment for evacuation planning and resource delivery. The aggregate model allocates populations in impacted facilities to alternate short-term facilities (e.g., hospitals), and a fine-grained extension for long-term senior-care facilities personalizes allocation using resident preferences, medical profiles, and social constraints. RADAR adapts as conditions evolve by utilizing historical data, live traffic, and changing facility status. We validated RADAR's efficacy in several disaster settings, including real events such as the Palisades wildfire and tabletop drills (earthquake and water-contamination scenarios) involving first responders.

IoT deployments in smart spaces can enable the development of useful services for their inhabitants. However, the diversity of smart spaces and their sensor infrastructures makes it challenging to develop space-agnostic applications. Moreover, existing schemas addressing interoperability challenges often lack the vocabulary needed to represent the integration of smart space systems and their inhabitants. We present a schema to annotate inhabited smart spaces in support of inhabitant-oriented applications. Our schema integrates well-known ontologies to represent inhabitants, events/activities, and the space itself, along with their interconnections. It also supports the representation of uncertain information from IoT and mobile sensors (e.g., a person's location or occupancy/attendance at an event). Additionally, we introduce an annotation tool that uses an easy-to-use GUI to describe a smart space based on our schema. We demonstrate the potential of our approach through a series of SPARQL queries and a system deployed at the UCI campus that annotates sensor data to support a space-agnostic occupancy monitoring application.

Data regulations like GDPR require systems to support data erasure but leave the definition of "erasure" open to interpretation. This ambiguity makes compliance challenging, especially in databases where data dependencies can lead to erased data being inferred from remaining data. We formally define a precise notion of data erasure that ensures any inference about deleted data, through dependencies, remains bounded to what could have been inferred before its insertion. We design erasure mechanisms that enforce this guarantee at minimal cost. Additionally, we explore strategies to balance cost and throughput, batch multiple erasures, and proactively compute data retention times when possible. We demonstrate the practicality and scalability of our algorithms using both real and synthetic datasets.

Time series imputation models have traditionally been developed using complete datasets with artificial masking patterns to simulate missing values. However, in real-world infrastructure monitoring, practitioners often encounter datasets where large amounts of data are missing and follow complex, heterogeneous patterns. We introduce DIM-SUM, a preprocessing framework for training robust imputation models that bridges the gap between artificially masked training data and real missing patterns. DIM-SUM combines pattern clustering and adaptive masking strategies with theoretical learning guarantees to handle diverse missing patterns actually observed in the data. Through extensive experiments on over 2 billion readings from California water districts, electricity datasets, and benchmarks, we demonstrate that DIM-SUM outperforms traditional methods by reaching similar accuracy with lower processing time and significantly less training data. When compared against a large pre-trained model, DIM-SUM averages 2x higher accuracy with significantly less inference time.

This paper presents Sentaur, a middleware designed, built, and deployed to support sensor-based smart space analytical applications. Sentaur supports a powerful data model that decouples semantic data (about the application domain) from sensor data (using which the semantic data is derived). By supporting mechanisms to map/translate data, concepts, and queries between the two levels, Sentaur relieves application developers from having to know or reason about either capabilities of sensors or write sensor specific code. This paper describes Sentaur's data model, its translation strategy, and highlights its benefits through real-world case studies.

In this paper, we address the challenges in supporting reliability and scalability in societal-scale notification systems that aim to reach large populations with customized alerts. We explore fault tolerance (FT) techniques in the context of Big Data Publish-Subscribe systems (BDPS), a scalable hierarchical architecture, that meshes big-data platforms (to store and operate on large volumes of data) with a distributed pub/sub broker network (to manage and communicate with a large number of end subscribers). The role of brokers in this architecture is critical since they serve to mediate interactions between subscribers and the backend big data system. We propose the REAPS (REliable Active Publish Subscribe) framework that can handle different classes of broker failures including randomized failures and geographically-correlated failures (as in a natural disaster). REAPS implements a low overhead fault tolerance service using a primary-backup approach; key features include the ability to exploit subscription similarity among brokers and techniques for quasi-active state replication to support fast recovery and delivery guarantees of notification services. We implement REAPS and conduct measurement studies on a prototype BDPS platform using real world usecases. We further evaluate REAPS under various failure scenarios to explore the scalability and performance of our proposed FT mechanisms via simulation studies.

The generalization of predictive models that follow the standard risk minimization paradigm of machine learning can be hindered by the presence of spurious correlations in the data. Identifying invariant predictors while training on data from multiple environments can influence models to focus on features that have an invariant causal relationship with the target, while reducing the effect of spurious features. Such invariant risk minimization approaches heavily rely on clearly defined environments and data being perfectly segmented into these environments for training. However, in real-world settings, perfect segmentation is challenging to achieve and these environment-aware approaches prove to be sensitive to segmentation errors. In this work, we present an environment-agnostic approach to develop generalizable models for classification tasks in sequential datasets without needing prior knowledge of environments. We show that our approach results in models that can generalize to out-of-distribution data and are not influenced by spurious correlations. We evaluate our approach on real-world sequential datasets from various domains.

WiFi connectivity events, generated when a mobile device connects to WiFi access points can serve as a robust, passive, (almost) zero-cost indoor localization technology. The challenge is the coarse level localization it offers that limits its usefulness. We recently developed a novel data cleaning based approach, LOCATER , that exploits patterns in the network data to achieve accuracy as high as 90% at room level granularity making it possible to use network data to support a much larger class of applications. In this paper, we demonstrate one such application to help organizations track levels of occupancy, and potential exposure of the inhabitants of the buildings to others possibly infected on their premises. The system, entitled T-Cove, is in operational use at over 20 buildings at UCI and has now become part of the reopening procedure of the schools. The demonstration will highlight T-Cove functionalities over both live data and data captured in the past.

This paper explores the data cleaning challenges that arise in using WiFi connectivity data to locate users to semantic indoor locations such as buildings, regions, rooms. WiFi connectivity data consists of sporadic connections between devices and nearby WiFi access points (APs), each of which may cover a relatively large area within a building. Our system, entitled semantic LOCATion cleanER (LOCATER), postulates semantic localization as a series of data cleaning tasks - first, it treats the problem of determining the AP to which a device is connected between any two of its connection events as a missing value detection and repair problem. It then associates the device with the semantic subregion (e.g., a conference room in the region) by postulating it as a location disambiguation problem. LOCATER uses a bootstrapping semi-supervised learning method for coarse localization and a probabilistic method to achieve finer localization. The paper shows that LOCATER can achieve significantly high accuracy at both the coarse and fine levels.

Nearly all of today's Big Data systems are passive in nature. We demonstrate our Big Active Data ("BAD") system, a scalable system that continuously and reliably captures Big Data and facilitates the timely and automatic delivery of new information to a large population of interested users as well as supporting analyses of historical information. We built our BAD project by extending an existing scalable, open-source BDMS (AsterixDB [1]) in this active direction. In this demonstration, we allow our audience to participate in an emergency notification application built on top of our BAD platform, and highlight its capabilities.

In this paper, we introduce Smart Source, a crowd sourcing based mobile Question & Answer (Q&A) system that aims to provide mobile information seekers with timely, trustworthy and accurate answers while ensuring that information providers are not inappropriately burdened. We tackle this challenge by taking advantage of both static and dynamic context and semantics from mobile users (e.g., Geolocation, social network, expertise/interest, device sensor profiles, battery level) to identify sources of information (i.e., Workers) that are trusted by the user and accurate enough for the questions at hand. Given a question, the Smart Source broker middleware executes a scalable and efficient worker selection algorithm that uses a Lyapunov optimization framework to maximize the utility of worker selection while guaranteeing the stability of the overall system. An associated assignor selection is used to scale the selection process to a large number of users. We implement the Smart Source prototype system on an Android test bed and thoroughly evaluate the system using real world applications and data, in particular those that involve geospatial questions and answers. Evaluation results indicate that Smart Source is efficient and provides superior worker selection compared to baseline approaches. Smart Source is also highly customizable: it employs a general utility function and provides a control knob to tradeoff the optimality and responding time. We believe that Smart Source will pave a way for new mechanisms of interaction among mobile users.

Associating textual annotations/tags with multimedia content is among the most effective approaches to organize and to support search over digital images and multimedia databases. Despite advances in multimedia analysis, effective tagging remains largely a manual process wherein users add descriptive tags by hand, usually when uploading or browsing the collection, much after the pictures have been taken. This approach, however, is not convenient in all situations or for many applications, e.g., when users would like to publish and share pictures with others in real time. An alternate approach is to instead utilize a speech interface using which users may specify image tags that can be transcribed into textual annotations by employing automated speech recognizers. Such a speech-based approach has all the benefits of human tagging without the cumbersomeness and impracticality typically associated with human tagging in real time. The key challenge in such an approach is the potential low recognition quality of the state-of-the-art recognizers, especially, in noisy environments. In this paper, we explore how semantic knowledge in the form of co-occurrence between image tags can be exploited to boost the quality of speech recognition. We postulate the problem of speech annotation as that of disambiguating among multiple alternatives offered by the recognizer. An empirical evaluation has been conducted over both real speech recognizer's output as well as synthetic data sets. The results demonstrate significant advantages of the proposed approach compared to the recognizer's output under varying conditions.

This paper considersthe problem of Multi-GeographyRoute Planning (MGRP) where the geographical information may be spread over multiple heterogeneous interconnected maps. Wefirstdesignaflexibleandscalablerepresentationtomodel individual geographies and their interconnections. Given such a representation, we develop an algorithm that exploits precomputation and caching of geographical data for path planning. A utility-based approach is adopted to decide which paths to precompute and store. To validate the proposed approach we test the algorithm over the workload of a campus level evacuation simulation that plans evacuation routes over multiple geographies: indoor CAD maps, outdoor maps, pedestrian and transportation networks, etc. The empirical results indicate that the MGRP algorithm withtheproposedutilitybasedcachingstrategysignificantly outperforms the state of the art solutions when applied to a large university campus data under varying conditions. 1.

In this paper, we have postulated the problem of using discrete speech utterances to annotate an image as that of disambiguation across multiple N-best lists. Our solution is based on the Maximum Entropy approach and uses correlations between tags in an existing corpus of images to set up the constrains of the corresponding constrained optimization problem. Our experiments suggest that the proposed approach gives a significant improvement in quality as compared to an approach that considers the best answer suggested by a popular off-the-shelf recognizer.

In this demonstration, we present the design and features of iDataGuard. iDataGuard is an interoperable security middleware that allows users to outsource their file systems to heterogeneous data storage providers available on the Internet. Examples of data storage providers include Amazon S3 service, Rapidshare. de and Nivarnix. In the iDataGuard architecture, data storage providers are untrusted. Therefore, iDataGuard preserves data confidentiality and integrity of outsourced information by using cryptographic techniques. iDataGuard effectively builds a secure network drive on top of any data storage provider on the Internet. We propose techniques that realize a secure file system over the heterogeneous data models offered by the diverse storage providers. iDataGuard significantly reduces the development effort required to build applications on top of the storage offered by the IDPs. Applications written to be compatible with iDataGuard, do not have to worry where the data is stored and how the security is enforced. iDataGuard automatically provides such functionality to application developers. To evaluate the practicality of iDataGuard, we implemented a version of the middleware layer to test its performance.

This paper addresses the issue of proxy-based data placement for mobile applications. The key idea is to use aggregated information about mobile users and their data request patterns to determine when, how frequently and how much data to be replicated on proxies. Specifically, we introduce a novel representation, mobile data overlay (MDO) that captures aggregated mobile data access patterns. The underlying representation of the MDO utilizes an interval tree-based data structure in supporting efficient spatio-temporal operations on mobile data access information. We develop intelligent MDO reconfiguration (ReC-MDO) techniques that help determine proper granularity of data replication operation (i.e. appropriate segmentation of each data object) by extracting spatio-temporal locality of mobile data access patterns. The ReC-MDO approach effectively balances tradeoffs between replication cost and data access cost in making mobile data placement decisions on proxies. Through extensive experimentation, we demonstrate the superiority of our techniques over several popular data placement strategies.

Most of the applications of wireless sensor networks involve primarily data collection with in-network processing in which continuous aggregate queries are posed and processed. There are two principle concerns with this type of applications. First, due to the use of batteries, limited power resource has been identified as a major challenge in deploying wireless sensor networks. Second, data is usually expected to be gathered as soon as possible to facilitate the monitoring of and the response to the physical phenomena. In this paper, we tackle these challenges through sensor state scheduling. The proposed technique is based on the observation that there are two types of traffic in sensor networks designed for data aggregation, bottom-up and top-down within an abstract tree structure. We show that it is possible to achieve deterministic schedules for data aggregation with very good performance. Specifically, we develop greedy algorithms to schedule transmission and listening operations for each sensor node to achieve collision- free communication. We show that the schedules can maximize the time sensor nodes spent on low-power states which helps achieve great energy efficiency, as well as allow fast data aggregation.

In this paper, we present SURCH, a novel decentralized algorithm for efficient processing of queries generated in sensor networks. Unlike existing techniques, SURCH is fully distributed and does not require the existence or construction of a communication infrastructure. It exploits the broadcast nature of wireless communication to optimize query propagation and evaluation. In SURCH, partial results are aggregated en route while the query spreads through the network. The key features of SURCH include its ability to avoid unnecessary communication, balanced node workload, and resilience to node failures. Performance results illustrate that SURCH outperforms alternative techniques for a variety of aggregation and selection queries

No abstract available.