Constantinos Costa

SmartTrace: Finding similar trajectories in smartphone networks without disclosing the traces

In this demonstration paper, we present a powerful distributed framework for finding similar trajectories in a smartphone network, without disclosing the traces of participating users. Our framework, exploits opportunistic and participatory sensing in order to quickly answer queries of the form: “Report objects (i.e., trajectories) that follow a similar spatio-temporal motion to Q, where Q is some query trajectory.” SmartTrace, relies on an in-situ data storage model, where geo-location data is recorded locally on smartphones for both performance and privacy reasons. SmartTrace then deploys an efficient top-K query processing algorithm that exploits distributed trajectory similarity measures, resilient to spatial and temporal noise, in order to derive the most relevant answers to Q quickly and efficiently. Our demonstration shows how the SmartTrace algorithmics are ported on a network of Android-based smartphone devices with impressive query response times. To demonstrate the capabilities of SmartTrace during the conference, we will allow the attendees to query local smartphone networks in the following two modes: i) Interactive Mode, where devices will be handed out to participants aiming to identify who is moving similar to the querying node; and ii) Trace-driven Mode, where a large-scale deployment can be launched in order to show how the K most similar trajectories can be identified quickly and efficiently. The conference attendees will be able to appreciate how interesting spatio-temporal search applications can be implemented efficiently (for performance reasons) and without disclosing the complete user traces to the query processor (for privacy reasons)1. For instance, an attendee might be able to determine other attendees that have participated in common sessions, in order to initiate new discussions and collaborations, without knowing their trajectory or revealing his/her own trajectory either.

Demo: a programming cloud of smartphones

In this demonstration we present SmartLab1, an exciting experimental testbed of approximately 40+ real Android Smartphones, plus emulated devices, deployed at the Department of Computer Science building at the University of Cyprus. SmartLab provides a public, permanent testbed for the development and testing of smartphone network applications via an intuitive web-based interface. Registered users can upload and install Android executables (APKs) on a number of Android smartphones, capture their output, reboot the devices, create concurrent interactive jobs using MonkeyRunner scripts, interact with the remote devices and many other exciting features. SmartLab aims to facilitate research in smartphone network programming environments, communication protocols, system design, and applications.

Distributed In-Memory Processing of All k Nearest Neighbor Queries

A wide spectrum of Internet-scale mobile applications, ranging from social networking, gaming and entertainment to emergency response and crisis management, all require efficient and scalable All k Nearest Neighbor (AkNN) computations over millions of moving objects every few seconds to be operational. Most traditional techniques for computing AkNN queries are centralized, lacking both scalability and efficiency. Only recently, distributed techniques for shared-nothing cloud infrastructures have been proposed to achieve scalability for large datasets. These batch-oriented algorithms are sub-optimal due to inefficient data space partitioning and data replication among processing units. In this paper, we present Spitfire , a distributed algorithm that provides a scalable and high-performance AkNN processing framework. Our proposed algorithm deploys a fast load-balanced partitioning scheme along with an efficient replication-set selection algorithm, to provide fast main-memory computations of the exact AkNN results in a batch-oriented manner. We evaluate, both analytically and experimentally, how the pruning efficiency of the Spitfire algorithm plays a pivotal role in reducing communication and response time up to an order of magnitude, compared to three other state-of-the-art distributed AkNN algorithms executed in distributed main-memory.

Efficient Exploration of Telco Big Data with Compression and Decaying

In the realm of smart cities, telecommunication companies (telcos) are expected to play a protagonistic role as these can capture a variety of natural phenomena on an ongoing basis, e.g., traffic in a city, mobility patterns for emergency response or city planning. The key challenges for telcos in this era is to ingest in the most compact manner huge amounts of network logs, perform big data exploration and analytics on the generated data within a tolerable elapsed time. This paper introduces SPATE, an innovative telco big data exploration framework whose objectives are two-fold: (i) minimizing the storage space needed to incrementally retain data over time, and (ii) minimizing the response time for spatiotemporal data exploration queries over recent data. The storage layer of our framework uses lossless data compression to ingest recent streams of telco big data in the most compact manner retaining full resolution for data exploration tasks. The indexing layer of our system then takes care of the progressive loss of detail in information, coined decaying, as data ages with time. The exploration layer provides visual means to explore the generated spatio-temporal information space. We measure the efficiency of the proposed framework using a 5GB anonymized real telco network trace and a variety of telco-specific tasks, such as OLAP and OLTP querying, privacy-aware data sharing, multivariate statistics, clustering and regression. We show that out framework can achieve comparable response times to the state-of-the-art using an order of magnitude less storage space.

Rayzit: An Anonymous and Dynamic Crowd Messaging Architecture

The smartphone revolution has introduced a new era of social networks where users communicate over anonymous messaging platforms to exchange opinions, ideas and even carry out commerce. These platforms enable individuals to establish social interactions between strangers based on a common interest or attribute. In this paper we present Rayzit1, a novel anonymous crowd messaging architecture, which utilizes the location of each user to connect them instantly to their k Nearest Neighbors (kNN) as they move in space. Contrary to the very large body of location-based social networks that suffer from bootstrapping issues, our architecture enables a user to always interact with the geographically closest possible users around. We establish this communication using a fast computation of an All kNN query that generates a dynamic global social graph every few seconds. We present motivating application scenarios and the detailed back-end architecture that allows Rayzit to scale. We have collected and analyzed data from the interactions of thousands of active users and confirm our claims.

Distributed in-memory processing of All K Nearest Neighbor queries

A wide spectrum of Internet-scale mobile applications, ranging from social networking, gaming and entertainment to emergency response and crisis management, all require efficient and scalable All k Nearest Neighbor (AkNN) computations over millions of moving objects every few seconds to be operational. Most traditional techniques for computing AkNN queries are centralized, lacking both scalability and efficiency. Only recently, distributed techniques for shared-nothing cloud infrastructures have been proposed to achieve scalability for large datasets. These batch-oriented algorithms are sub-optimal due to inefficient data space partitioning and data replication among processing units. In this paper, we present Spitfire, a distributed algorithm that provides a scalable and high-performance AkNN processing framework. Our proposed algorithm deploys a fast load-balanced partitioning scheme along with an efficient replication-set selection algorithm, to provide fast main-memory computations of the exact AkNN results in a batch-oriented manner. We evaluate, both analytically and experimentally, how the pruning efficiency of the Spitfire algorithm plays a pivotal role in reducing communication and response time up to an order of magnitude, compared to three other state-of-the-art distributed AkNN algorithms executed in distributed main-memory.

SPATE: Compacting and Exploring Telco Big Data

In this demonstration paper, we present SPATE, an innovative telco big data exploration framework whose objectives are two-fold: (i) minimizing the storage space needed to incrementally retain data over time, and (ii) minimizing the response time for spatiotemporal data exploration queries over stored data. Our framework deploys lossless data compression to ingest streams of telco big data in the most compact manner retaining full resolution for data exploration tasks. We augment our storage structures with decaying principles that lead to the progressive loss of detail as information gets older. Our framework also includes visual and declarative interfaces for a variety of telco-specific data exploration tasks. We demonstrate SPATE in two modes: (i) Visual Mode, where attendees will be able to interactively explore synthetic telco traces we will provide, and (ii) SQL Mode, where attendees can submit custom SQL queries based on a provided schema.

Crowdsourcing emergency data in non-operational cellular networks

In overloaded or partially broken (i.e., non-operational) cellular networks, it is imperative to enable communication within the crowd to allow the management of emergency and crisis situations. To this end, a variety of emerging short-range communication technologies available on smartphones, such as, Wi-Fi Direct, 3G/LTE direct or Bluetooth/BLE, are able to enable users nowadays to shape point-to-point communication among them. These technologies, however, do not support the formation of overlay networks that can be used to gather and transmit emergency response state (e.g., transfer the location of trapped people to nearby people or the emergency response guard). In this paper, we develop techniques that generate the k-Nearest-Neighbor (kNN) overlay graph of an arbitrary crowd that interconnects over some short-range communication technology. Enabling a kNN overlay graph allows the crowd to connect to its geographically closest peers, those that can physically interact with the user and respond to an emergency crowdsourcing task, such as seeing/sensing similar things as the user (e.g., collect videos and photos). It further allows for intelligent synthesis and mining of heterogeneous data based on the computed kNN graph of the crowd to extract valuable real-time information. We particularly present two efficient algorithms, namely Akin+ and Prox+, which are optimized to work on a resource-limited mobile device. We use Rayzit, a real-world crowd messaging framework we develop, as an example that operates on a kNN graph to motivate and evaluate our work. We use mobility traces collected from three sources for evaluation. The results show that Akin+ and Prox+ significantly outperform existing algorithms in efficiency, even under a skewed distribution of users.