Query: Let's go public! taking a spoken dialog system to the real world

Relevant passages:
Ravenclaw: dialog management using hierarchical task decomposition and an expectation agenda <=SEP=> We describe RavenClaw, a new dialog management framework developed as a successor to the Agenda [1] architecture used in the CMU Communicator. RavenClaw introduces a clear separation between task and discourse behavior specification, and allows rapid development of dialog management components for spoken dialog systems operating in complex, goal-oriented domains. The system development effort is focused entirely on the specification of the dialog task, while a rich set of domain-independent conversational behaviors are transparently generated by the dialog engine. To date, RavenClaw has been applied to five different domains allowing us to draw some preliminary conclusions as to the generality of the approach. We briefly describe our experience in developing these systems.

Stochastic Language Generation For Spoken Dialogue Systems <=SEP=> The two current approaches to language generation, Template-based and rule-based (linguistic) NLG, have limitations when applied to spoken dialogue systems, in part because they were developed for text generation. In this paper, we propose a new corpus-based approach to natural language generation, specifically designed for spoken dialogue systems.

Automatically clustering similar units for unit selection in speech synthesis <=SEP=> This paper describes a new method for synthesizing speech by concatenating sub-word units from a database of labelled speech. A large unit inventory is created by automatically clustering units of the same phone class based on their phonetic and prosodic context. The appropriate cluster is then selected for a target unit offering a small set of candidate units. An optimal path is found through the candidate units based on their distance from the cluster center and an acoustically based join cost. Details of the method and justification are presented. The results of experiments using two different databases are given, optimising various parameters within the system. Also a comparison with other existing selection based synthesis techniques is given showing the advantages this method has over existing ones. The method is implemented within a full text-to-speech system offering efficient natural sounding speech synthesis.

Creating natural dialogs in the carnegie mellon communicator system <=SEP=> The Carnegie Mellon Communicator system helps users create complex travel itineraries through a conversational interface. Itineraries consist of (multi-leg) flights, hotel and car reservations and are built from actual travel information for North America, obtained from the Web. The system manages dialog using a schema-based approach. Schemas correspond to major units of task information (such as a flight leg) and define conversational topics, or foci of interaction, meaningful to the user.

Extraction of high confidence minutiae points from fingerprint images <=SEP=> Fingerprint is one of the well accepted biometric traits for building automatic human recognition system. These systems mainly involves matching of two fingerprints based on their minutiae points. Therefore, extraction of reliable or true minutiae from the fingerprint image is critically important. This paper proposed an novel algorithm for automatic extraction of highly reliable minutiae points from a fingerprint image. It utilizes frequency domain image enhancement and heuristics. Experiments have been conducted on two databases. FVC2002 which is a publicly available fingerprint database containing 800 fingerprint images of 100 persons and IITK-Sel500FP which is an in-house database containing 1000 fingerprint images of 500 persons. Minutiae points extracted by the proposed algorithm are benchmarked against the manually marked ones and the comparison is done with an open source minutiae extractor mindtct of NIST. Experimental results have shown that the proposed algorithm has significantly higher confidence of extracted minutiae points being true.

Cognitive effort drives workspace configuration of human brain functional networks. <=SEP=> Effortful cognitive performance is theoretically expected to depend on the formation of a global neuronal workspace. We tested specific predictions of workspace theory, using graph theoretical measures of network topology and physical distance of synchronization, in magnetoencephalographic data recorded from healthy adult volunteers (N = 13) during performance of a working memory task at several levels of difficulty. We found that greater cognitive effort caused emergence of a more globally efficient, less clustered, and less modular network configuration, with more long-distance synchronization between brain regions. This pattern of task-related workspace configuration was more salient in the β-band (16-32 Hz) and γ-band (32-63 Hz) networks, compared with both lower (α-band; 8-16 Hz) and higher (high γ-band; 63-125 Hz) frequency intervals. Workspace configuration of β-band networks was also greater in faster performing participants (with correct response latency less than the sample median) compared with slower performing participants. Processes of workspace formation and relaxation in relation to time-varying demands for cognitive effort could be visualized occurring in the course of task trials lasting <2 s. These experimental results provide support for workspace theory in terms of complex network metrics and directly demonstrate how cognitive effort breaks modularity to make human brain functional networks transiently adopt a more efficient but less economical configuration.

Designing wireless transceiver blocks for LoRa application <=SEP=> The Internet of Things enables connected objects to communicate over distant fields, and trade-offs are often observed with the battery life and range. Long Range Radio, or LoRa, provides transmission for up to 15 km with impressive battery life, while sacrificing throughput. In order to design the LoRa system, there is a need to design and implement the LoRa transceiver. 1 V, 900 MHz, LoRa-capable transceiver blocks were designed in 65 nm process. The receiver front end consists of the narrowband low noise amplifier, common gate-common source balun, and double balanced mixer. The power consumption of this system is 2.63 mW. The overall gain of the receiver front end is approximately 51 dB. The transmitter implemented in this project utilizes a class-D amplifier alongside an inverter chain driver stage and a transformer based power combiner. The transmitter outputs 21.57 dBm of power from the given supply at 900 MHz, operating at 21% drain efficiency.

Network Monitoring as a Streaming Analytics Problem <=SEP=> Programmable switches potentially make it easier to perform flexible network monitoring queries at line rate, and scalable stream processors make it possible to fuse data streams to answer more sophisticated queries about the network in real-time. However, processing such network monitoring queries at high traffic rates requires both the switches and the stream processors to filter the traffic iteratively and adaptively so as to extract only that traffic that is of interest to the query at hand. While the realization that network monitoring is a streaming analytics problem has been made earlier, our main contribution in this paper is the design and implementation of Sonata, a closed-loop system that enables network operators to perform streaming analytics for network monitoring applications at scale. To achieve this objective, Sonata allows operators to express a network monitoring query by considering each packet as a tuple. More importantly, Sonata allows them to partition the query across both the switches and the stream processor, and through iterative refinement, Sonata's runtime attempts to extract only the traffic that pertains to the query, thus ensuring that the stream processor can scale to satisfy a large number of queries for traffic at very high rates. We show with a simple example query involving DNS reflection attacks and traffic traces from one of the world's largest IXPs that Sonata can capture 95% of all traffic pertaining to the query, while reducing the overall data rate by a factor of about 400 and the number of required counters by four orders of magnitude.

Neural representations for object perception: structure, category, and adaptive coding. <=SEP=> Object perception is one of the most remarkable capacities of the primate brain. Owing to the large and indeterminate dimensionality of object space, the neural basis of object perception has been difficult to study and remains controversial. Recent work has provided a more precise picture of how 2D and 3D object structure is encoded in intermediate and higher-level visual cortices. Yet, other studies suggest that higher-level visual cortex represents categorical identity rather than structure. Furthermore, object responses are surprisingly adaptive to changes in environmental statistics, implying that learning through evolution, development, and also shorter-term experience during adulthood may optimize the object code. Future progress in reconciling these findings will depend on more effective sampling of the object domain and direct comparison of these competing hypotheses.

A Wireless Headstage for Combined Optogenetics and Multichannel Electrophysiological Recording <=SEP=> This paper presents a wireless headstage with real-time spike detection and data compression for combined optogenetics and multichannel electrophysiological recording. The proposed headstage, which is intended to perform both optical stimulation and electrophysiological recordings simultaneously in freely moving transgenic rodents, is entirely built with commercial off-the-shelf components, and includes 32 recording channels and 32 optical stimulation channels. It can detect, compress and transmit full action potential waveforms over 32 channels in parallel and in real time using an embedded digital signal processor based on a low-power field programmable gate array and a Microblaze microprocessor softcore. Such a processor implements a complete digital spike detector featuring a novel adaptive threshold based on a Sigma-delta control loop, and a wavelet data compression module using a new dynamic coefficient re-quantization technique achieving large compression ratios with higher signal quality. Simultaneous optical stimulation and recording have been performed in-vivo using an optrode featuring 8 microelectrodes and 1 implantable fiber coupled to a 465-nm LED, in the somatosensory cortex and the Hippocampus of a transgenic mouse expressing ChannelRhodospin (Thy1::ChR2-YFP line 4) under anesthetized conditions. Experimental results show that the proposed headstage can trigger neuron activity while collecting, detecting and compressing single cell microvolt amplitude activity from multiple channels in parallel while achieving overall compression ratios above 500. This is the first reported high-channel count wireless optogenetic device providing simultaneous optical stimulation and recording. Measured characteristics show that the proposed headstage can achieve up to 100% of true positive detection rate for signal-to-noise ratio (SNR) down to 15 dB, while achieving up to 97.28% at SNR as low as 5 dB. The implemented prototype features a lifespan of up to 105 minutes, and uses a lightweight (2.8 g) and compact  $(17 \times 18 \times 10\ \text{mm}^{3})$ rigid-flex printed circuit board.

Big Data and Transformational Government <=SEP=> The big data phenomenon is growing throughout private and public sector domains. Profit motives make it urgent for companies in the private sector to learn how to leverage big data. However, in the public sector, government services could also be greatly improved through the use of big data. Here, the authors describe some drivers, barriers, and best practices affecting the use of big data and associated analytics in the government domain. They present a model that illustrates how big data can result in transformational government through increased efficiency and effectiveness in the delivery of services. Their empirical basis for this model uses a case vignette from the US Department of Veterans Affairs, while the theoretical basis is a balanced view of big data that takes into account the continuous growth and use of such data. This article is part of a special issue on big data and business analytics.

An analysis and critique of Research through Design: towards a formalization of a research approach <=SEP=> The field of HCI is experiencing a growing interest in Research through Design (RtD), a research approach that employs methods and processes from design practice as a legitimate method of inquiry. We are interested in expanding and formalizing this research approach, and understanding how knowledge, or theory, is generated from this type of design research. We conducted interviews with 12 leading HCI design researchers, asking them about design research, design theory, and RtD specifically. They were easily able to identify different types of design research and design theory from contemporary and historical design research efforts, and believed that RtD might be one of the most important contributions of design researchers to the larger research community. We further examined three historical RtD projects that were repeatedly mentioned in the interviews, and performed a critique of current RtD practices within the HCI research and interaction design communities. While our critique summarizes the problems, it also shows possible directions for further developments and refinements of the approach.

SDN On-The-Go (OTG) physical testbed <=SEP=> An emerging field of research, Software Defined Networks (SDN) promises to change the landscape of traditional network topology and management. Researchers and early adopters alike need adequate SDN testing facilities for their experiments but their options are limited. Industry is responding slowly with embedded support for SDN in their enterprise grade network hardware but it is cost prohibitive for many test environments with a single SDN switch costing thousands of dollars. There are a few emerging community SDN test networks that are fantastic for testing large topologies with production grade traffic but there is a cost associated with membership and some controlled experiments are difficult. A free and indispensible alternative to a dedicated hardware SDN is to use network emulation tools. These software tools are widely used and invaluable to SDN research. They provide an amazingly precise representation of physical network nodes and behavior but are inherently limited by their aggregation with other virtual devices on the same compute node. Some of our research requires a higher precision than software emulation can provide. Our solution is to build a low cost, portable, standalone SDN testbed. Called SDN On-The-Go (OTG), it is a complete, self-contained testbed that consists of four dedicated ZodiacFX SDN switches, four RaspberryPi3 hosts, a dedicated Kangaroo+ controller with 4GB RAM and a couple of routers to form the network isolation. The testbed supports many configurations for pseudo real-world SDN experiments that produce reliable and repeatable results. It can be used as a standalone research tool or as part of a larger network with production quality traffic. SDN OTG is designed to be used as a portable teaching device, moved from classroom to classroom or taken home for private research. We achieved our repeatability factor of an order of magnitude greater than emulation based testing. Our SDN OTG physical testbed weighs only twenty pounds, costs about a thousand US dollars, provides repeatable, precise time sensitive data and can be setup as a fully functional SDN testbed in a matter of minutes.

Predicting and Improving Memory Retention : Psychological Theory Matters in the Big Data Era <=SEP=> Cognitive psychology has long had the aim of understanding mechanisms of human memory, with the expectation that such an understanding will yield practical techniques that support learning and retention. Although research insights have given rise to qualitative advice for students and educators, we present a complementary approach that offers quantitative, individualized guidance. Our approach synthesizes theory-driven and data-driven methodologies. Psychological theory characterizes basic mechanisms of human memory shared among members of a population, whereas machine-learning techniques use observations from a population to make inferences about individuals. We argue that despite the power of big data, psychological theory provides essential constraints on models. We present models of forgetting and spaced practice that predict the dynamic time-varying knowledge state of an individual student for specific material. We incorporate these models into retrieval-practice software to assist students in reviewing previously mastered material. In an ambitious year-long intervention in a middle-school foreign language course, we demonstrate the value of systematic review on long-term educational outcomes, but more specifically, the value of adaptive review that leverages data from a population of learners to personalize recommendations based on an individual’s study history and past performance.

IoT survey: An SDN and fog computing perspective <=SEP=> Recently, there has been an increasing interest in the Internet of Things (IoT). While some analysts disvalue the IoT hype, several technology leaders, governments, and researchers are putting serious efforts to develop solutions enabling wide IoT deployment. Thus, the huge amount of generated data, the high network scale, the security and privacy concerns, the new requirements in terms of QoS, and the heterogeneity in this ubiquitous network of networks make its implementation a very challenging task. SDN, a new networking paradigm, has revealed its usefulness in reducing the management complexities in today’s networks. Additionally, SDN, having a global view of the network, has presented effective security solutions. On the other hand, fog computing, a new data service platform, consists of pushing the data to the network edge reducing the cost (in terms of bandwidth consumption and high latency) of “big data” transportation through the core network. In this paper, we critically review the SDN and fog computingbased solutions to overcome the IoT main challenges, highlighting their advantages, and exposing their weaknesses. Thus, we make recommendations at the end of this paper for the upcoming research work. Keywords— IoT; Survey; SDN; Fog; Cloud; 5G.

General Image-Quality Equation: GIQE. <=SEP=> A regression-based model was developed relating aerial image quality, expressed in terms of the National Imagery Interpretability Rating Scale (NIIRS), to fundamental image attributes. The General Image-Quality Equation (GIQE) treats three main attributes: scale, expressed as the ground-sampled distance; sharpness, measured from the system modulation transfer function; and the signal-to-noise ratio. The GIQE can be applied to any visible sensor and predicts NIIRS ratings with a standard error of 0.3 NIIRS. The image attributes treated by the GIQE are influenced by system design and operation parameters. The GIQE allows system designers and operators to perform trade-offs for the optimization of image quality.

A Survey on Quality of Experience of HTTP Adaptive Streaming <=SEP=> Changing network conditions pose severe problems to video streaming in the Internet. HTTP adaptive streaming (HAS) is a technology employed by numerous video services that relieves these issues by adapting the video to the current network conditions. It enables service providers to improve resource utilization and Quality of Experience (QoE) by incorporating information from different layers in order to deliver and adapt a video in its best possible quality. Thereby, it allows taking into account end user device capabilities, available video quality levels, current network conditions, and current server load. For end users, the major benefits of HAS compared to classical HTTP video streaming are reduced interruptions of the video playback and higher bandwidth utilization, which both generally result in a higher QoE. Adaptation is possible by changing the frame rate, resolution, or quantization of the video, which can be done with various adaptation strategies and related client- and server-side actions. The technical development of HAS, existing open standardized solutions, but also proprietary solutions are reviewed in this paper as fundamental to derive the QoE influence factors that emerge as a result of adaptation. The main contribution is a comprehensive survey of QoE related works from human computer interaction and networking domains, which are structured according to the QoE impact of video adaptation. To be more precise, subjective studies that cover QoE aspects of adaptation dimensions and strategies are revisited. As a result, QoE influence factors of HAS and corresponding QoE models are identified, but also open issues and conflicting results are discussed. Furthermore, technical influence factors, which are often ignored in the context of HAS, affect perceptual QoE influence factors and are consequently analyzed. This survey gives the reader an overview of the current state of the art and recent developments. At the same time, it targets networking researchers who develop new solutions for HTTP video streaming or assess video streaming from a user centric point of view. Therefore, this paper is a major step toward truly improving HAS.

A Framework for End-to-End Evaluation of 5G mmWave Cellular Networks in ns-3 <=SEP=> The growing demand for ubiquitous mobile data services along with the scarcity of spectrum in the sub-6 GHz bands has given rise to the recent interest in developing wireless systems that can exploit the large amount of spectrum available in the millimeter wave (mmWave) frequency range. Due to its potential for multi-gigabit and ultra-low latency links, mmWave technology is expected to play a central role in 5th Generation (5G) cellular networks. Overcoming the poor radio propagation and sensitivity to blockages at higher frequencies presents major challenges, which is why much of the current research is focused at the physical layer. However, innovations will be required at all layers of the protocol stack to effectively utilize the large air link capacity and provide the end-to-end performance required by future networks.
 Discrete-event network simulation will be an invaluable tool for researchers to evaluate novel 5G protocols and systems from an end-to-end perspective. In this work, we present the first-of-its-kind, open-source framework for modeling mmWave cellular networks in the ns-3 simulator. Channel models are provided along with a configurable physical and MAC-layer implementation, which can be interfaced with the higher-layer protocols and core network model from the ns-3 LTE module to simulate end-to-end connectivity. The framework is demonstrated through several example simulations showing the performance of our custo mmmWave stack.

Center Frequency and Bandwidth Controllable Microstrip Bandpass Filter Design Using Loop-Shaped Dual-Mode Resonator <=SEP=> A design approach for developing microwave bandpass filters (BPFs) with continuous control of the center frequency and bandwidth is presented. The proposed approach exploits a simple loop-shaped dual-mode resonator that is tapped and perturbed with varactor diodes to realize center frequency tunability and passband reconfigurabilty. The even- and odd-mode resonances of the resonator can be predominately controlled via the incorporated varactors, and the passband response reconfiguration is obtained with the proposed tunable external coupling mechanism, which resolves the return losses degradation attributed to conventional fixed external coupling mechanisms. The demonstrated approach leads to a relatively simple synthesis of the microwave BPF with an up to 33% center frequency tuning range, an excellent bandwidth tuning capability, as well as a high filter response reconfigurability, including an all-reject response.

Gated Siamese Convolutional Neural Network Architecture for Human Re-Identification <=SEP=> Matching pedestrians across multiple camera views, known as human re-identification, is a challenging research problem that has numerous applications in visual surveillance. With the resurgence of Convolutional Neural Networks (CNNs), several end-to-end deep Siamese CNN architectures have been proposed for human re-identification with the objective of projecting the images of similar pairs (i.e. same identity) to be closer to each other and those of dissimilar pairs to be distant from each other. However, current networks extract fixed representations for each image regardless of other images which are paired with it and the comparison with other images is done only at the final level. In this setting, the network is at risk of failing to extract finer local patterns that may be essential to distinguish positive pairs from hard negative pairs. In this paper, we propose a gating function to selectively emphasize such fine common local patterns by comparing the mid-level features across pairs of images. This produces flexible representations for the same image according to the images they are paired with. We conduct experiments on the CUHK03, Market-1501 and VIPeR datasets and demonstrate improved performance compared to a baseline Siamese CNN architecture.

Data association for multi-object Tracking-by-Detection in multi-camera networks <=SEP=> Multi-object tracking is still a challenging task in computer vision. We propose a robust approach to realize multi-object tracking using multi-camera networks. Detection algorithms are utilized to detect object regions with confidence scores for initialization of individual particle filters. Since data association is the key issue in Tracking-by-Detection mechanism, we present an efficient greedy matching algorithm considering multiple judgments based on likelihood functions. Furthermore, tracking in single cameras is realized by a greedy matching method. Afterwards, 3D geometry positions are obtained from the triangulation relationship between cameras. Corresponding objects are tracked in multiple cameras to take the advantages of multi-camera based tracking. Our algorithm performs online and does not need any information about the scene, no restrictions of enter-and-exit zones, no assumption of areas where objects are moving on and can be extended to any class of object tracking. Experimental results show the benefits of using multiple cameras by the higher accuracy and precision rates.

A real-time method for depth enhanced visual odometry <=SEP=> Visual odometry can be augmented by depth information such as provided by RGB-D cameras, or from lidars associated with cameras. However, such depth information can be limited by the sensors, leaving large areas in the visual images where depth is unavailable. Here, we propose a method to utilize the depth, even if sparsely available, in recovery of camera motion. In addition, the method utilizes depth by structure from motion using the previously estimated motion, and salient visual features for which depth is unavailable. Therefore, the method is able to extend RGBD visual odometry to large scale, open environments where depth often cannot be sufficiently acquired. The core of our method is a bundle adjustment step that refines the motion estimates in parallel by processing a sequence of images, in a batch optimization. We have evaluated our method in three sensor setups, one using an RGB-D camera, and two using combinations of a camera and a 3D lidar. Our method is rated #4 on the KITTI odometry benchmark irrespective of sensing modality—compared to stereo visual odometry methods which retrieve depth by triangulation. The resulting average position error is 1.14% of the distance traveled.

Repeatability, reproducibility and rigor in systems research <=SEP=> Computer systems research spans sub-disciplines that include embedded and real-time systems, compilers, networking, and operating systems. Our contention is that a number of structural factors inhibit quality research and decrease the velocity of science. We highlight some of the factors we have encountered in our work and observed in published papers and propose solutions that, if widely adopted, could both increase the productivity of researchers and the quality of their output.

Earpod: eyes-free menu selection using touch input and reactive audio feedback <=SEP=> We present the design and evaluation of earPod: an eyes-free menu technique using touch input and reactive auditory feedback. Studies comparing earPod with an iPod-like visual menu technique on reasonably-sized static menus indicate that they are comparable in accuracy. In terms of efficiency (speed), earPod is initially slower, but outperforms the visual technique within 30 minutes of practice. Our results indicate that earPod is potentially a reasonable eyes-free menu technique for general use, and is a particularly exciting technique for use in mobile device interfaces.

Incorporating Luminance, Depth and Color Information by Fusion-based Networks for Semantic Segmentation <=SEP=> Semantic segmentation has made encouraging progress due to the success of deep convolutional networks in recent years. Meanwhile, depth sensors become prevalent nowadays, so depth maps can be acquired more easily. However, there are few studies that focus on the RGB-D semantic segmentation task. Exploiting the depth information effectiveness to improve performance is a challenge. In this paper, we propose a novel solution named LDFNet, which incorporates Luminance, Depth and Color information by a fusion-based network. It includes a sub-network to process depth maps and employs luminance images to assist the depth information in processes. LDFNet outperforms the other state-of-art systems on the Cityscapes dataset, and its inference speed is faster than most of the existing networks. The experimental results show the effectiveness of the proposed multi-modal fusion network and its potential for practical applications.

Applying Reinforcement Learning to Blackjack Using Q-Learning <=SEP=> Blackjack is a popular card game played in many casinos. The objective of the game is to win money by obtaining a point total higher than the dealer’s without exceeding 21. Determining an optimal blackjack strategy proves to be a difficult challenge due to the stochastic nature of the game. This presents an interesting opportunity for machine learning algorithms. Supervised learning techniques may provide a viable solution, but do not take advantage of the inherent reward structure of the game. Reinforcement learning algorithms generally perform well in stochastic environments, and could utilize blackjack's reward structure. This paper explores reinforcement learning as a means of approximating an optimal blackjack strategy using the Q-learning algorithm.

Methods of inference and learning for performance modeling of parallel applications <=SEP=> Increasing system and algorithmic complexity combined with a growing number of tunable application parameters pose significant challenges for analytical performance modeling. We propose a series of robust techniques to address these challenges. In particular, we apply statistical techniques such as clustering, association, and correlation analysis, to understand the application parameter space better. We construct and compare two classes of effective predictive models: piecewise polynomial regression and artifical neural networks. We compare these techniques with theoretical analyses and experimental results. Overall, both regression and neural networks are accurate with median error rates ranging from 2.2 to 10.5 percent. The comparable accuracy of these models suggest differentiating features will arise from ease of use, transparency, and computational efficiency.

Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC <=SEP=> State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning in nonlinear nonparametric statespace models. We place a Gaussian process prior over the transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. However, to enable efficient inference, we marginalize over the dynamics of the model and instead infer directly the joint smoothing distribution through the use of specially tailored Particle Markov Chain Monte Carlo samplers. Once an approximation of the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. We make use of sparse Gaussian process models to greatly reduce the computational complexity of the approach.

Spatial and Spatio-Temporal Multidimensional Data Modelling: A Survey <=SEP=> Data warehouse store and provide access to large volume of historical data supporting the strategic decisions of organisations. Data warehouse is based on a multidimensional model which allow to express user’s needs for supporting the decision making process. Since it is estimated that 80% of data used for decision making has a spatial or location component, spatial data have been widely integrated in Data Warehouses and in OLAP systems. Extending a multidimensional data model by the inclusion of spatial data provides a concise and organised spatial data warehouse representation. This paper aims to provide a comprehensive review of literature on developed and suggested spatial and spatio-temporel multidimensional models. A benchmarking study of the proposed models is presented. Several evaluation criteria’s are used to identify the existence of trends as well as potential needs for further investigations. Keywords— Data warehouse, Spatial data, Multidimensional modelling, Temporal data