Infrastructure-based Indoor Positioning Systems (IIPS) have emerged as critical components of wireless deployments for many enterprises so as to track mobile devices without additional device-side applications or computation. To provide transformative location-based services, it is important that IIPS compute accurate locations over a long period of time, scaling with a large number of tracked devices cost-effectively. In this paper, we present MotionScanner, which includes novel feature-based and end-to-end deep learning motion detection models to detect device motion solely from noisy, temporally sparse, and partial Wi-Fi measurements at access points. We further integrate MotionScanner into an IIPS so that the IIPS can exploit previously computed locations effectively to improve location accuracy, and skip unnecessary location computation. Building on observations of how location estimates of stationary devices scatter over time, we can monitor and enhance the performance of IIPS. We evaluate MotionScanner with data sets collected from real-world deployments of IIPS at two enterprises, and show that MotionScanner achieves 83% motion detection accuracy while saving 80% of computational resources.

Association rule mining typically focuses on discovering global rules valid across the entire dataset. Yet local rules valid for subsets of the dataset, while significantly different from global rules, are often also of tremendous importance to analysts. In this work, we tackle this overlooked problem of online mining of localized asso-ciation rules. We provide support for analysts to interactively mine rules that are hidden in a global context yet are locally significant. To tackle this problem we design a compact multidimensional itemset-based data partitioning (MIP-index). MIP-index offers ef-ficient mining performance by utilizing precomputed results, while still allowing the user the flexibility of selecting any data subset of interest at run-time. We design a suite of alternative execu-tion strategies for processing such localized mining requests. Op-timization principles such as selection push-up, supported R-tree filter and differential treatment of contained and partially over-lapped MIPs are proposed. We analytically and experimentally demonstrate that different execution strategies are effective for dif-ferent query scenarios. Given a localized mining query, our CO-LARM query optimizer takes a cost-based approach to identify the best strategy for execution. Through extensive experiments using benchmark data sets we demonstrate that the COLARM optimizer is highly accurate in online plan selection and discovering local-ized rules (otherwise hidden in the global context) in a diversity of localized mining requests.

Smartphones can collect considerable context data about the user, ranging from apps used to places visited. Frequent user patterns discovered from longitudinal, multi-modal context data could help personalize and improve overall user experience. Our long term goal is to develop novel middleware and algorithms to efficiently mine user behavior patterns entirely on the phone by utilizing idle processor cycles. Mining patterns on the mobile device provides better privacy guarantees to users, and reduces dependency on cloud connectivity. As an important step in this direction, we develop a novel general-purpose service called MobileMiner that runs on the phone and discovers frequent co-occurrence patterns indicating which context events frequently occur together. Using longitudinal context data collected from 106 users over 1--3 months, we show that MobileMiner efficiently generates patterns using limited phone resources. Further, we find interesting behavior patterns for individual users and across users, ranging from calling patterns to place visitation patterns. Finally, we show how our co-occurrence patterns can be used by developers to improve the phone UI for launching apps or calling contacts.

We demonstrate our SPIRE technology for supporting interactive mining of both positive and negative rules at the speed of thought. It is often misleading to learn only about positive rules, yet extremely revealing to find strongly supported negative rules. Key technical contributions of SPIRE including region-wise abstractions of rules, positive-negative rule relationship analysis, rule redundancy management and rule visualization supporting novel exploratory queries will be showcased. The audience can interactively explore complex rule relationships in a visual manner, such as comparing negative rules with their positive counterparts, that would otherwise take prohibitive time. Overall, our SPIRE system provides data analysts with rich insights into rules and rule relationships while significantly reducing manual effort and time investment required.

While significant strides have been made on efficient association rule mining, the usability of mining systems woefully lags behind. In particular, the usability of rule mining systems is limited by the lack of support for interactive exploration of the relationships among rule results produced with various parameter settings. Based on a novel parameter space-driven approach, our proposed Framework for Interactive Rule Exploration (FIRE) addresses the usability shortcoming. FIRE features innovative visual displays and effective interactions that enable analysts to conduct rule exploration at the speed of thought. Particularly, the parameter space view (PSpace) displays the distribution of rules produced for diverse parameter settings. This not only facilitates user parameter selection but also empowers analyst's to understand rule relationships in the parameter space context. Our user study with 22 subjects establishes the usability and effectiveness of the proposed features and interactions of FIRE using benchmark datasets. Overall, this research encompasses significant contributions at the intersection of data mining, knowledge management and visual analytics.

We demonstrate our SPHINX system that not only derives but also visualizes evidence-hypotheses relationships on a parameter space of belief and plausibility. SPHINX facilitates the analyst to interactively explore the contribution of different pieces of evidence towards the hypotheses. The key technical contributions of SPHINX include both computational and visual dimensions. The computational contributions cover (a.) flexible computational model selection; and (b.) real-time incremental strength computations. The visual contributions include (a.) sense-making over parameter space; (b.) filtering and abstraction options; (c.) novel visual displays such as evidence glyph and skyline views. Using two real datasets, we will demonstrate that the SPHINX system provides the analysts with rich insights into evidence-hypothesis relationships facilitating the discovery and decision making process.

We demonstrate our PARAS technology for supporting interactive association mining at near real-time speeds. Key technical innovations of PARAS, in particular, stable region abstractions and rule redundancy management supporting novel parameter space-centric exploratory queries will be showcased. The audience will be able to interactively explore the parameter space view of rules. They will experience near real-time speeds achieved by PARAS for operations, such as comparing rule sets mined using different parameter values, that would otherwise take hours of computation and much manual investigation. Overall, we will demonstrate that the PARAS system provides a rich experience to data analysts through parameter tuning recommendations while significantly reducing the trial-and-error interactions.

Association rule mining is known to be computationally intensive, yet real-time decision-making applications are increasingly intolerant to delays. In this paper, we introduce the parameter space model, called PARAS. PARAS enables efficient rule mining by compactly maintaining the final rulesets. The PARAS model is based on the notion of stable region abstractions that form the coarse granularity ruleset space. Based on new insights on the redundancy relationships among rules, PARAS establishes a surprisingly compact representation of complex redundancy relationships while enabling efficient redundancy resolution at query-time. Besides the classical rule mining requests, the PARAS model supports three novel classes of exploratory queries. Using the proposed PSpace index, these exploratory query classes can all be answered with near real-time responsiveness. Our experimental evaluation using several benchmark datasets demonstrates that PARAS achieves 2 to 5 orders of magnitude improvement over state-of-the-art approaches in online association rule mining.

Continuous time-series sequence matching, specifically, matching a numeric live stream against a set of redefined pattern sequences, is critical for domains ranging from fire spread tracking to network traffic monitoring. While several algorithms exist for similarity matching of static time-series data, matching continuous data poses new, largely unsolved challenges including online real-time processing requirements and system resource limitations for handling infinite streams. In this work, we propose a novel live stream matching framework, called n-Snippet Indices Framework (in short, SNIF), to tackle these challenges. SNIF employs snippets as the basic unit for matching streaming time-series. The insight is to perform the matching at two levels of granularity: bag matching of subsets of snippets of the live stream against prefixes of the patterns, and order checking for maintaining successive candidate snippet bag matches. We design a two-level index structure, called SNIF index, which supports these two modes of matching. We propose a family of online two-level prefix matching algorithms that trade off between result accuracy and response time. The effectiveness of SNIF to detect patterns has been thoroughly tested through experiments using real datasets from the domains of fire monitoring and sensor motes. In this paper, we also present a study of SNIF's performance, accuracy and tolerance to noise compared against those of the state-of-the-art Continuous Query with Prediction (CQP) approach.

This demonstration presents FireStream, a sensor stream processing system which provides services for run-time detection, monitoring and visualization of fire spread in intelligent buildings that can be of great benefit to first responders. Our system can effectively handle large heterogeneous sensor streams using shared window execution and dynamic participant handling to yield a high-ary MJoin solution.