This paper lays the ground work for assistive navigation using wearable sensors and social sensors to foster situational awareness for the blind. Our system acquires social media messages to gauge the relevant aspects of an event and to create alerts. We propose social semantics that captures the parameters required for querying and reasoning an event-of-interest, such as what, where, who, when, severity, and action from the Internet of things, using an event summarization algorithm. Our approach integrates wearable sensors in the physical world to estimate user location based on metric and landmark localization. Streaming data from the cyber world are employed to provide awareness by summarizing the events around the user based on the situation awareness factor. It is illustrated using disaster and socialization event scenarios. Discovered local events are fed back using sound localization so that the user can actively participate in a social event or get early warning of any hazardous events. A feasibility evaluation of our proposed algorithm included comparing the output of the algorithm to ground truth, a survey with sighted participants about the algorithm output, and a sound localization user interface study with blind-folded sighted participants. Thus, our framework supports the navigation problem for the blind by combining the advantages of our real-time localization technologies so that the user is being made aware of the world, a necessity for independent travel.

In this paper we investigate the problem of processing multi-way interval joins on map-reduce platform. We look at join queries formed by interval predicates as defined by Allen’s interval algebra. These predicates can be classified in two groups: colocation based predicates and sequence based predicates. A colocation predicate requires two intervals to share at least one common point while a sequence predi-cate requires two intervals to be disjoint. An interval join query can therefore be thought of as belonging to one of the three classes: (a) queries containing only colocation based predicates, (b) queries containing only sequence based pred-icates and (c) queries containing both classes of predicates. We address these three classes of join queries, discuss the challenges and present novel approaches for processing these queries on map-reduce platform. We also discuss why the current approaches developed for handling join queries on real-valued data can not be directly used to handle inter-val joins. We finally extend the approaches developed to handle join queries containing multiple interval attributes as well as join queries containing both interval as well as non-interval attributes. Through experimental evaluations both on synthetic and real life datasets, we demonstrate that the proposed approaches comfortably outperform naive ap-proaches. 1.

In this paper we investigate the problem of processing multi-way spatial joins on map-reduce platform. We look at two common spatial predicates - overlap and range. We address these two classes of join queries, discuss the challenges and outline novel approaches for executing these queries on a map-reduce framework. We then discuss how we can process join queries involving both overlap and range predicates. Specifically we present a Controlled-Replicate framework using which we design the approaches presented in this paper. The Controlled-Replicate framework is carefully engineered to minimize the communication among cluster nodes. Through experimental evaluations we discuss the complexity of the problem under investigation, details of Controlled-Replicate framework and demonstrate that the proposed approaches comfortably outperform naive approaches.

Data quality is a perennial problem for many enterprise data assets. To improve data quality, businesses often employ rule based data standardization systems in which domain experts code rules for handling important and prevalent patterns. Finding these patterns is laborious and time consuming, particularly for noisy or highly specialized data sets. It is also subjective to the persons determining these patterns. In this paper we present a tool to automatically mine patterns that can help in improving the efficiency and effectiveness of these data standardization systems. The automatically extracted patterns are used by the domain and knowledge experts for rule writing. We use a greedy algorithm to extract patterns that result in a maximal coverage of data. We further group the extracted patterns such that each group represents patterns that capture similar domain knowledge. We propose a similarity measure that uses input pattern semantics to group these patterns. We demonstrate the effectiveness of our method for standardization tasks on three real world datasets.