Query: Iterative Hough Forest with Histogram of Control Points for 6 DoF object registration from depth images

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3D object recognition in range images using visibility context <=SEP=> Recognizing and localizing queried objects in range images plays an important role for robotic manipulation and navigation. Even though it has been steadily studied, it is still a challenging task for scenes with occlusion and clutter.

Go-ICP: Solving 3D Registration Efficiently and Globally Optimally <=SEP=> Registration is a fundamental task in computer vision. The Iterative Closest Point (ICP) algorithm is one of the widely-used methods for solving the registration problem. Based on local iteration, ICP is however well-known to suffer from local minima. Its performance critically relies on the quality of initialization, and only local optimality is guaranteed. This paper provides the very first globally optimal solution to Euclidean registration of two 3D point sets or two 3D surfaces under the L2 error. Our method is built upon ICP, but combines it with a branch-and-bound (BnB) scheme which searches the 3D motion space SE(3) efficiently. By exploiting the special structure of the underlying geometry, we derive novel upper and lower bounds for the ICP error function. The integration of local ICP and global BnB enables the new method to run efficiently in practice, and its optimality is exactly guaranteed. We also discuss extensions, addressing the issue of outlier robustness.

Voting-based pose estimation for robotic assembly using a 3D sensor <=SEP=> We propose a voting-based pose estimation algorithm applicable to 3D sensors, which are fast replacing their 2D counterparts in many robotics, computer vision, and gaming applications. It was recently shown that a pair of oriented 3D points, which are points on the object surface with normals, in a voting framework enables fast and robust pose estimation. Although oriented surface points are discriminative for objects with sufficient curvature changes, they are not compact and discriminative enough for many industrial and real-world objects that are mostly planar. As edges play the key role in 2D registration, depth discontinuities are crucial in 3D. In this paper, we investigate and develop a family of pose estimation algorithms that better exploit this boundary information. In addition to oriented surface points, we use two other primitives: boundary points with directions and boundary line segments. Our experiments show that these carefully chosen primitives encode more information compactly and thereby provide higher accuracy for a wide class of industrial parts and enable faster computation. We demonstrate a practical robotic bin-picking system using the proposed algorithm and a 3D sensor.

Surface reconstruction from unorganized points <=SEP=> Computer Graphics, 26, 2, July 1992 Surface Reconstruction from Unorganized Points Hugues Hoppe* Tony DeRose* Tom Duchampt John McDonald$ Werner Stuetzle~ University of Washington Seattle, WA 98195 We describe and demonstrate an algorithm that takes as input an unorganized set of points {xl, . . . . x.} c IR3 on or near an unknown manifold M, and produces as output a simplicial surface that approximates M. Neither the topology, the presence of boundaries, nor the geometry of M are assumed to be known in advance — all are inferred automatically from the data. This problem natu rally arises in a variety of practical situations such as range scanning an object from multiple view points, recovery of biological shapes from two-dimensional slices, and interactive surface sketching. CR

Registration and Integration of Textured 3-D Data <=SEP=> In general, multiple views are required to create a complete 3-D model of an object or of a multi-roomed indoor scene. In this work, we address the problem of merging multiple textured 3-D data sets, each of which corresponds to a different view of a scene or object. There are two steps to the merging process: registration and integration. To register, or align, data sets we use a modified version of the Iterative Closest Point algorithm; our version, which we call color ICP, considers not only 3-D information, but color as well. We show experimentally that the use of color decreases registration error significantly. Once the 3-D data sets have been registered, we integrate them to produce a seamless, composite 3-D textured model. Our approach to integration uses a 3-D occupancy grid to represent likelihood of spatial occupancy through voting. In addition to occupancy information, we store surface normal in each voxel of the occupancy grid. Surface normal is used to robustly extract a surface from the occupancy grid; on that surface we blend textures from multiple views.

Neem and other Botanical insecticides: Barriers to commercialization <=SEP=> In spite of the wide recognition that many plants possess insecticidal properties, only a handful of pest control products directly obtained from plants,i. e., botanical insecticides, are in use in developed countries. The demonstrated efficacy of the botanical neem (based on seed kernel extracts ofAzadirachta indica), and its recent approval for use in the United States, has stimulated research and development of other botanical insecticides. However, the commercialization of new botanical insecticides can be hindered by a number of issues. The principal barriers to commercialization of new botanicals are (i) scarcity of the natural resource; (ii) standardization and quality control; and (iii) registration. These issues are no problem (i) or considerably less of a problem (ii, iii) with conventional insecticides. In this review I discuss these issues and suggest how the problems may be overcome in the future.

Trajectory learning from human demonstrations via manifold mapping <=SEP=> This work proposes a framework that enables arbitrary robots with unknown kinematics models to imitate human demonstrations to acquire a skill, and reproduce it in real-time. The diversity of robots active in non-laboratory environments is growing constantly, and to this end we present an approach for users to be able to easily teach a skill to a robot with any body configuration. Our proposed method requires a motion trajectory obtained from human demonstrations via a Kinect sensor, which is then projected onto a corresponding human skeleton model. The kinematics mapping between the robot and the human model is learned by employing Local Procrustes Analysis, which enables the transfer of the demonstrated trajectory from the human model to the robot. Finally, the transferred trajectory is modeled using Dynamic Movement Primitives, allowing it to be reproduced in real time. Experiments in simulation on a 4 degree of freedom robot show that our method is able to correctly imitate various skills demonstrated by a human.

A Chinese Text Corrector Based on Seq2Seq Model <=SEP=> In this paper, we build a Chinese text corrector which can correct spelling mistakes precisely in Chinese texts. Our motivation is inspired by the recently proposed seq2seq model which consider the text corrector as a sequence learning problem. To begin with, we propose a biased-decoding method to improve the bilingual evaluation understudy (BLEU) score of our model. Secondly, we adopt a more reasonable OOV token scheme, which enhances the robustness of our correction mechanism. Moreover, to test the performance of our proposed model thoroughly, we establish a corpus which includes 600,000 sentences from news data of Sogou Labs. Experiments show that our corrector model can achieve better corrector results based on the corpus.

Cloud Log Forensics Metadata Analysis <=SEP=> The increase in the quantity and questionable quality of the forensic information retrieved from the current virtualized data cloud system architectures has made it extremely difficult for law enforcement to resolve criminal activities within these logical domains. This paper poses the question of what kind of information is desired from virtual machine (VM) hosted operating systems (OS) investigated by a cloud forensic examiner. The authors gives an overview of the information that exists on current VM OS by looking at it's kernel hypervisor logs and discusses the shortcomings. An examination of the role that the VM kernel hypervisor logs provide as OS metadata in cloud investigations is also presented.

A novel wideband slotted mm wave microstrip patch antenna <=SEP=> In this paper, a novel approach for the design of a compact broadband planar microstrip patch antenna is presented for millimetre wave wireless applications. This antenna structure was realized on a Quartz crystal substrate of dielectric constant 3.8 with the thickness of 0.8mm with ground plane. In order to examine, the performances of this antenna it was designed at the frequency 30 GHz and simulated by IE3D software package of Zeland. This type of antenna is composed of two slots of different dimensions and fed by single coaxial feed. Appropriate design of the antenna structure resulted in three discontinuous resonant bands. The proposed antenna has a −10dB return loss impedance bandwidth of 10.3GHz. The performance of this antenna was verified and characterised in terms of the antenna return loss, radiation pattern, surface current distribution and power gain. Also, the numerical results show that the proposed antenna has good impedance bandwidth and radiation characteristics over the entire operating bands. Details of the proposed antenna configurations and design procedures are given.

Mobile Application Security in the Presence of Dynamic Code Updates <=SEP=> The increasing number of repeated malware penetrations into official mobile app markets poses a high security threat to the confidentiality and privacy of end users’ personal and sensitive information. Protecting end user devices from falling victims to adversarial apps presents a technical and research challenge for security researchers/engineers in academia and industry. Despite the security practices and analysis checks deployed at app markets, malware sneak through the defenses and infect user devices. The evolution of malware has seen it become sophisticated and dynamically changing software usually disguised as legitimate apps. Use of highly advanced evasive techniques, such as encrypted code, obfuscation and dynamic code updates, etc., are common practices found in novel malware. With evasive usage of dynamic code updates, a malware pretending as benign app bypasses analysis checks and reveals its malicious functionality only when installed on a user’s device. This dissertation provides a thorough study on the use and the usage manner of dynamic code updates in Android apps. Moreover, we propose a hybrid analysis approach, StaDART, that interleaves static and dynamic analysis to cover the inherent shortcomings of static analysis techniques to analyze apps in the presence of dynamic code updates. Our evaluation results on real world apps demonstrate the effectiveness of StaDART. However, typically dynamic analysis, and hybrid analysis too for that matter, brings the problem of stimulating the app’s behavior which is a non-trivial challenge for automated analysis tools. To this end, we propose a backward slicing based targeted inter component code paths execution technique, TeICC. TeICC leverages a backward slicing mechanism to extract code paths starting from a target point in the app. It makes use of a system dependency graph to extract code paths that involve inter component communication. The extracted code paths are then instrumented and executed inside the app context to capture sensitive dynamic behavior, resolve dynamic code updates and obfuscation. Our evaluation of TeICC shows that it can be effectively used for targeted execution of inter component code paths in obfuscated Android apps. Also, still not ruling out the possibility of adversaries reaching the user devices, we propose an on-phone API hooking

PCAS: Pruning Channels with Attention Statistics for Deep Network Compression <=SEP=> Compression techniques for deep neural networks are important for implementing them on small embedded devices. In particular, channel-pruning is a useful technique for realizing compact networks. However, many conventional methods require manual setting of compression ratios in each layer. It is difficult to analyze the relationships between all layers, especially for deeper models. To address these issues, we propose a simple channel-pruning technique based on attention statistics that enables to evaluate the importance of channels. We improved the method by means of a criterion for automatic channel selection, using a single compression ratio for the entire model in place of per-layer model analysis. The proposed approach achieved superior performance over conventional methods with respect to accuracy and the computational costs for various models and datasets. We provide analysis results for behavior of the proposed criterion on different datasets to demonstrate its favorable properties for channel pruning.

An automated domain specific stop word generation method for natural language text classification <=SEP=> We propose an automated method for generating domain specific stop words to improve classification of natural language content. Also we implemented a Bayesian natural language classifier working on web pages, which is based on maximum a posteriori probability estimation of keyword distributions using bag-of-words model to test the generated stop words. We investigated the distribution of stop-word lists generated by our model and compared their contents against a generic stop-word list for English language. We also show that the document coverage rank and topic coverage rank of words belonging to natural language corpora follow Zipf's law, just like the word frequency rank is known to follow.

Image-Dependent Gamut Mapping as Optimization Problem <=SEP=> We explore the potential of image-dependent gamut mapping as a constrained optimization problem. The performance of our new approach is compared to standard reference gamut mapping algorithms in psycho-visual tests.

Research synthesis in software engineering: A tertiary study <=SEP=> 0950-5849/$ see front matter 2011 Elsevier B.V. A doi:10.1016/j.infsof.2011.01.004 ⇑ Corresponding author. E-mail addresses: dcruzes@idi.ntnu.no (D.S. Cruzes Context: Comparing and contrasting evidence from multiple studies is necessary to build knowledge and reach conclusions about the empirical support for a phenomenon. Therefore, research synthesis is at the center of the scientific enterprise in the software engineering discipline. Objective: The objective of this article is to contribute to a better understanding of the challenges in synthesizing software engineering research and their implications for the progress of research and practice. Method: A tertiary study of journal articles and full proceedings papers from the inception of evidencebased software engineering was performed to assess the types and methods of research synthesis in systematic reviews in software engineering. Results: As many as half of the 49 reviews included in the study did not contain any synthesis. Of the studies that did contain synthesis, two thirds performed a narrative or a thematic synthesis. Only a few studies adequately demonstrated a robust, academic approach to research synthesis. Conclusion: We concluded that, despite the focus on systematic reviews, there is limited attention paid to research synthesis in software engineering. This trend needs to change and a repertoire of synthesis methods needs to be an integral part of systematic reviews to increase their significance and utility for research and practice. 2011 Elsevier B.V. All rights reserved.

Vishit: A Visualizer for Hindi Text <=SEP=> We outline the design of a visualizer, named Vishit, for texts in the Hindi language. The Hindi language is lingua franca in many states of India where people speak different languages. The visualized text serves as a universal language where seamless communication is needed by many people who speak different languages and have different cultures. Vishit consists of the following three major processing steps: language processing, knowledge base creation and scene generation. Initial results from the Vishit prototype are encouraging.

Software-Based Resolver-to-Digital Conversion Using a DSP <=SEP=> A simple and cost-effective software-based resolver-to-digital converter using a digital signal processor is presented. The proposed method incorporates software generation of the resolver carrier using a digital filter for synchronous demodulation of the resolver outputs in such a way that there is a substantial savings on hardware like the costly carrier oscillator and associated digital and analog circuits for amplitude demodulators. In addition, because the method does not cause any time delay, the dynamics of the servo control using the scheme are not affected. Furthermore, the method enables the determination of the angle for a complete 360deg shaft rotation with reasonable accuracy using a lookup table that contains entries of only up to 45deg. Computer simulations and experimental results demonstrate the effectiveness and applicability of the proposed scheme.

Learning to Detect Misleading Content on Twitter <=SEP=> The publication and spread of misleading content is a problem of increasing magnitude, complexity and consequences in a world that is increasingly relying on user-generated content for news sourcing. To this end, multimedia analysis techniques could serve as an assisting tool to spot and debunk misleading content on the Web. In this paper, we tackle the problem of misleading multimedia content detection on Twitter in real time. We propose a number of new features and a new semi-supervised learning event adaptation approach that helps generalize the detection capabilities of trained models to unseen content, even when the event of interest is of different nature compared to that used for training. Combined with bagging, the proposed approach manages to outperform previous systems by a significant margin in terms of accuracy. Moreover, in order to communicate the verification process to end users, we develop a web-based application for visualizing the results.

Directional antennas in FANETs: A performance analysis of routing protocols <=SEP=> Flying Ad-hoc Networks (FANETs) [1] are mobile ad-hoc networks formed by small and medium-sized UAVs. Nowadays, most UAVs are equipped with omnidirectional antennas. In addition, most of the existing routing protocols were designed assuming the use of omnidirectional antennas. Directional antennas have the potential to increase spatial reuse, save the battery's energy, and substantially increase the transmission range. However, these benefits come with a few challenges. Existing directional MAC protocols deal with these challenges, mostly in static ad-hoc networks. We define DA-FANETs as FANETs where directional antennas are used. In this paper, we investigate the performance of existing routing protocols in DA-FANETs. First we implement an 802.11b-based directional MAC protocol that embodies common directional MAC protocol features. Then we evaluate the following routing protocols in DA-FANET scenarios: AODV, OLSR, RGR, and GRP. The results show that each antenna beamwidth has an optimal network size that gives the highest amount of routing. In addition, RGR is the best option for DA-FANETs while GRP is the worst. Although RGR shows the best performance of all, it still leaves a lot of room for improvement with a PDR sitting at just 85% and a relatively high amount of overhead in terms of the number of transmissions performed.

A new 6-DOF parallel robot with simple kinematic model <=SEP=> This paper presents a novel six-legged parallel robot, the kinematic model of which is simpler than that of the simplest six-axis serial robot. The new robot is the 6-DOF extension of the Cartesian parallel robot. It consists of three pairs of base-mounted prismatic actuators, the directions in each pair parallel to one of the axes of a Cartesian coordinate system. In each of the six legs, there are also two passive revolute joints, the axes of which are parallel to the direction of the prismatic joint. Finally, each leg is attached to the mobile platform via a spherical joint. The direct kinematics of the novel parallel robot can be solved easily by partitioning the orientation and the position of the mobile platform. There are eight distinct solutions, which can be found directly by solving a linear system and alternating the signs of three radicals. This parallel robot has a large workspace and is suitable for machining or rapid prototyping, as detailed in this paper.

Accurate Singular Values of Bidiagonal Matrices <=SEP=> C Abstract om puting the singular valu es of a bidiagonal m atrix is the fin al phase of the stan dard algo-w rithm for the singular valu e decom position of a general m atrix. We present a ne w algorithm hich com putes all the singular valu es of a bidiagonal m atrix to high relative accuracy indepen-p dent of their m agn itudes. In contrast, the stan dard algorithm for bidiagonal m atrice s m ay com ute sm all singular valu es with no relative accuracy at all. Num erical experim ents sh ow th at K the new algorithm is com parable in speed to the stan dard algorithm , an d frequently faster. The stan dard algorithm for com puting the singular valu e decom position (SVD) of a gen-1 eral real m atrix A has two phases [7]:) Com pute orthogonal m atrices P an d Q su ch that B = P AQ is in bidiagonal form , i.e. 1 1 1 T 1. 2 has nonzero entries only on its diagonal an d first su perdiagonal) Com pute orthogonal m atrices P an d Q su ch that Σ = P BQ is diagonal an d nonnega-t i 2 2 2 T 2 ive. The diagonal entries σ of Σ are the singular values of A. We will take them to be-sorted in decreasing order: σ ≥ σ. The colum ns of Q = Q Q are the right singular vec i i + 1 1 2 t 1 2 ors, an d the colum ns of P= P P are the left singular vectors. n This process takes O (n) operations, where n is the dim ension of A. This is true eve 3 though Phase 2 is iterative, since it converges quickly in practice. The error an alysis o f this r u com bined procedure has a widely accepted conclusion [8], an d provided neither overflow no nderflow occurs m ay be su m m arized as follows: The com puted singular valu es σ differ from the true singular valu es of A by no m ore t 1 i han p (n). ε. A , where A = σ is the 2-norm of A, ε is the m ach ine precision, an d p (n) is a T slowly growing function of the dim ension n …

Learning Symbolic Models of Stochastic Domains <=SEP=> In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a probabilistic, relational planning rule representation that compactly models noisy, nondeterministic action effects, and show how such rules can be effectively learned. Through experiments in simple planning domains and a 3D simulated blocks world with realistic physics, we demonstrate that this learning algorithm allows agents to effectively model world dynamics.

A Rank Minimization Heuristic with Application to Minimum Order System Approximation <=SEP=> Several problems arising in control system analysis and design, such as reduced order controller synthesis, involve minimizing the rank of a matrix variable subject to linear matrix inequality (LMI) constraints. Except in some special cases, solving this rank minimization problem (globally) is very difficult. One simple and surprisingly effective heuristic, applicable when the matrix variable is symmetric and positive semidefinite, is to minimize its trace in place of its rank. This results in a semidefinite program (SDP) which can be efficiently solved. In this paper we describe a generalization of the trace heuristic that applies to general nonsymmetric, even non-square, matrices, and reduces to the trace heuristic when the matrix is positive semidefinite. The heuristic is to replace the (nonconvex) rank objective with the sum of the singular values of the matrix, which is the dual of the spectral norm. We show that this problem can be reduced to an SDP, hence efficiently solved. To motivate the heuristic, we show that the dual spectral norm is the convex envelope of the rank on the set of matrices with norm less than one. We demonstrate the method on the problem of minimum order system approximation.

StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation <=SEP=> Existing open-domain human-computer conversation systems are typically passive: they either synthesize or retrieve a reply provided a human-issued utterance. It is generally presumed that humans should take the role to lead the conversation and introduce new content when a stalemate occurs, and that the computer only needs to “respond.” In this paper, we propose STALEMATEBREAKER, a conversation system that can proactively introduce new content when appropriate. We design a pipeline to determine when, what, and how to introduce new content during human-computer conversation. We further propose a novel reranking algorithm BiPageRank-HITS to enable rich interaction between conversation context and candidate replies. Experiments show that both the content-introducing approach and the reranking algorithm are effective. Our full STALEMATEBREAKER model outperforms a state-of-the-practice conversation system by +14.4% p@1 when a stalemate occurs.

Ethical theory, codes of ethics and IS practice <=SEP=> Ethical issues, with respect to computer-based information systems, are important to the individual IS practitioner. These same issues also have an important impact on the moral well-being of organizations and societies. Considerable discussion has taken place in the Information Systems (IS) literature on specific ethical issues, but there is little published work which relates these issues to mainstream ethical theory. This paper describes a range of ethical theories drawn from philosophy literature and uses these theories to critique aspects of the newly revised ACM Code of Ethics. Some in the IS field on problematic ethical issues which are not resolved by the Code are then identified and discussed. The paper draws some implications and conclusions on the value of ethical theory and codes of practice, and on further work to develop existing ethical themes and to promote new initiatives in the ethical domain.

Dual-camera design for coded aperture snapshot spectral imaging. <=SEP=> Coded aperture snapshot spectral imaging (CASSI) provides an efficient mechanism for recovering 3D spectral data from a single 2D measurement. However, since the reconstruction problem is severely underdetermined, the quality of recovered spectral data is usually limited. In this paper we propose a novel dual-camera design to improve the performance of CASSI while maintaining its snapshot advantage. Specifically, a beam splitter is placed in front of the objective lens of CASSI, which allows the same scene to be simultaneously captured by a grayscale camera. This uncoded grayscale measurement, in conjunction with the coded CASSI measurement, greatly eases the reconstruction problem and yields high-quality 3D spectral data. Both simulation and experimental results demonstrate the effectiveness of the proposed method.

Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets <=SEP=> C product reviews have proliferated online, driven by the notion that consumers’ decision to purchase or not purchase a product is based on the positive or negative information about that product they obtain from fellow consumers. Using research on information processing as a foundation, we suggest that in the context of an online community, reviewer disclosure of identity-descriptive information is used by consumers to supplement or replace product information when making purchase decisions and evaluating the helpfulness of online reviews. Using a unique data set based on both chronologically compiled ratings as well as reviewer characteristics for a given set of products and geographical location-based purchasing behavior from Amazon, we provide evidence that community norms are an antecedent to reviewer disclosure of identity-descriptive information. Online community members rate reviews containing identity-descriptive information more positively, and the prevalence of reviewer disclosure of identity information is associated with increases in subsequent online product sales. In addition, we show that shared geographical location increases the relationship between disclosure and product sales, thus highlighting the important role of geography in electronic commerce. Taken together, our results suggest that identity-relevant information about reviewers shapes community members’ judgment of products and reviews. Implications for research on the relationship between online word-of-mouth (WOM) and sales, peer recognition and reputation systems, and conformity to online community norms are discussed.

Examining CNN Representations With Respect to Dataset Bias <=SEP=> Given a pre-trained CNN without any testing samples, this paper proposes a simple yet effective method to diagnose feature representations of the CNN. We aim to discover representation flaws caused by potential dataset bias. More specifically, when the CNN is trained to estimate image attributes, we mine latent relationships between representations of different attributes inside the CNN. Then, we compare the mined attribute relationships with ground-truth attribute relationships to discover the CNN’s blind spots and failure modes due to dataset bias. In fact, representation flaws caused by dataset bias cannot be examined by conventional evaluation strategies based on testing images, because testing images may also have a similar bias. Experiments have demonstrated the effectiveness of our method.

A hybrid Fuzzy Logic Controller-Firefly Algorithm (FLC-FA) based for MPPT Photovoltaic (PV) system in solar car <=SEP=> This paper propose Firefly Algorithm (FA) new method for tuning the membership function of fuzzy logic controller for Maximum Power Point Tracker (MPPT) system Photovoltaic solar car, that consist of hybrid Fuzzy Logic Controller - Firefly Algorithm (FLC-FA) for the parameter. There are many MPPT methods for photovoltaic (PV) system, Perturbation and Observation (PnO), fuzzy logic controller (FLC) standard, and hybrid Fuzzy logic controller firefly algorithm (FLC-FA) is compared in this paper. The proposed FLC-FA algorithm is to obtain the optimal solution for MPPT for photovoltaic (PV) systems for solar cars. The result Fuzzy logic controller firefly (FLC-FA) of the proposed method, the highest maximum strength and efficiency generated is PnO = 96.31%, Standard FLC = 99.88% and Proposed FLC-FA = 99.98%. better than PnO and Fuzzy logic controller standard method. The main advantage of the proposed FLC-FA is more efficient and accurate than the still fuzzy logic controller standard.

A review and future direction of agile, business intelligence, analytics and data science <=SEP=> Agile methodologies were introduced in 2001. Since this time, practitioners have applied Agile methodologies to many delivery disciplines. This article explores the application of Agile methodologies and principles to business intelligence delivery and how Agile has changed with the evolution of business intelligence. Business intelligence has evolved because the amount of data generated through the internet and smart devices has grown exponentially altering how organizations and individuals use information. The practice of business intelligence delivery with an Agile methodology has matured; however, business intelligence has evolved altering the use of Agile principles and practices. The Big Data phenomenon, the volume, variety, and velocity of data, has impacted business intelligence and the use of information. New trends such as fast analytics and data science have emerged as part of business intelligence. This paper addresses how Agile principles and practices have evolved with business intelligence, as well as its challenges and future directions.