Query: Live acquisition of main memory data from Android smartphones and smartwatches

Relevant passages:
Acquisition and analysis of volatile memory from android devices <=SEP=> The Android operating system for mobile phones, which is still relatively new, is rapidly gaining market share, with dozens of smartphones and tablets either released or set to be released. In this paper, we present the first methodology and toolset for acquisition and deep analysis of volatile physical memory from Android devices. The paper discusses some of the challenges in performing Android memory acquisition, discusses our new kernel module for dumping memory, named dmd, and specifically addresses the difficulties in developing device-independent acquisition tools. Our acquisition tool supports dumping memory to either the SD on the phone or via the network. We also present analysis of kernel structures using newly developed Volatility functionality. The results of this work illustrate the potential that deep memory analysis offers to digital forensics investigators. a 2011 Elsevier Ltd. All rights reserved.

New acquisition method based on firmware update protocols for Android smartphones <=SEP=> Android remains the dominant OS in the smartphone market even though the iOS share of the market increased during the iPhone 6 release period. As various types of Android smartphones are being launched in the market, forensic studies are being conducted to test data acquisition and analysis. However, since the application of new Android security technologies, it has become more difficult to acquire data using existing forensic methods. In order to address this problem, we propose a new acquisition method based on analyzing the firmware update protocols of Android smartphones. A physical acquisition of Android smartphones can be achieved using the flash memory read command by reverse engineering the firmware update protocol in the bootloader. Our experimental results demonstrate that the proposed method is superior to existing forensic methods in terms of the integrity guarantee, acquisition speed, and physical dump with screen-locked smartphones (USB debugging disabled). © 2015 The Authors. Published by Elsevier Ltd on behalf of DFRWS. This is an open access articleunder theCCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Forensic analysis of social networking applications on mobile devices <=SEP=> The increased use of social networking applications on smartphones makes these devices a goldmine for forensic investigators. Potential evidence can be held on these devices and recovered with the right tools and examination methods. This paper focuses on conducting forensic analyses on three widely used social networking applications on smartphones: Facebook, Twitter, and MySpace. The tests were conducted on three popular smartphones: BlackBerrys, iPhones, and Android phones. The tests consisted of installing the social networking applications on each device, conducting common user activities through each application, acquiring a forensically sound logical image of each device, and performing manual forensic analysis on each acquired logical image. The forensic analyses were aimed at determining whether activities conducted through these applications were stored on the device’s internal memory. If so, the extent, significance, and location of the data that could be found and retrieved from the logical image of each device were determined. The results show that no traces could be recovered from BlackBerry devices. However, iPhones and Android phones store a significant amount of valuable data that could be recovered and used by forensic investigators. a 2012 A. Marrington, N. Al Mutawa & I. Baggili. Published by Elsevier Ltd. All rights

Howard: A Dynamic Excavator for Reverse Engineering Data Structures <=SEP=> Even the most advanced reverse engineering techniques and products are weak in recovering data structures in stripped binaries—binaries without symbol tables. Unfortunately, forensics and reverse engineering without data structures is exceedingly hard. We present a new solution, known as Howard, to extract data structures from C binaries without any need for symbol tables. Our results are significantly more accurate than those of previous methods — sufficiently so to allow us to generate our own (partial) symbol tables without access to source code. Thus, debugging such binaries becomes feasible and reverse engineering becomes simpler. Also, we show that we can protect existing binaries from popular memory corruption attacks, without access to source code. Unlike most existing tools, our system uses dynamic analysis (on a QEMU-based emulator) and detects data structures by tracking how a program uses memory.

iPhone 3 GS Forensics : Logical analysis using Apple iTunes Backup Utility <=SEP=> The iPhone mobile is used worldwide due to its enhanced computing capabilities, increased storage capacity as well as its attractive touch interface. These characteristics made the iPhone a popular smart phone device. The increased use of the iPhone lead it to become a potential source of digital evidence in criminal investigations. Therefore, iPhone forensics turned into an essential practice for forensic and security practitioners today. This research aimed at investigating and examining the logical backup acquisition of the iPhone 3GS mobile device using the Apple iTunes backup utility. It was found that significant data of forensic value such as e-mail messages, text and multimedia messages, calendar events, browsing history, GPRS locations, contacts, call history and voicemail recording can be retrieved using this method of iPhone acquisition.

Training and operation of an integrated neuromorphic network based on metal-oxide memristors <=SEP=> Despite much progress in semiconductor integrated circuit technology, the extreme complexity of the human cerebral cortex, with its approximately 1014 synapses, makes the hardware implementation of neuromorphic networks with a comparable number of devices exceptionally challenging. To provide comparable complexity while operating much faster and with manageable power dissipation, networks based on circuits combining complementary metal-oxide-semiconductors (CMOSs) and adjustable two-terminal resistive devices (memristors) have been developed. In such circuits, the usual CMOS stack is augmented with one or several crossbar layers, with memristors at each crosspoint. There have recently been notable improvements in the fabrication of such memristive crossbars and their integration with CMOS circuits, including first demonstrations of their vertical integration. Separately, discrete memristors have been used as artificial synapses in neuromorphic networks. Very recently, such experiments have been extended to crossbar arrays of phase-change memristive devices. The adjustment of such devices, however, requires an additional transistor at each crosspoint, and hence these devices are much harder to scale than metal-oxide memristors, whose nonlinear current–voltage curves enable transistor-free operation. Here we report the experimental implementation of transistor-free metal-oxide memristor crossbars, with device variability sufficiently low to allow operation of integrated neural networks, in a simple network: a single-layer perceptron (an algorithm for linear classification). The network can be taught in situ using a coarse-grain variety of the delta rule algorithm to perform the perfect classification of 3 × 3-pixel black/white images into three classes (representing letters). This demonstration is an important step towards much larger and more complex memristive neuromorphic networks.

A Natural Language Tutorial Dialogue System for Physics <=SEP=> We describe theWHY2-ATLAS intelligent tutoring system for qualitative physics that interacts with students via natural language dialogue. We focus on the issue of analyzing and responding to multi-sentential explanations. We explore approaches for achieving a deeper understanding of these explanations and dialogue management approaches and strategies for providing appropriate feedback on them.

Nonword repetition and sentence repetition as clinical markers of specific language impairment: the case of Cantonese. <=SEP=> PURPOSE
Recent research suggests that nonword repetition (NWR) and sentence repetition (SR) tasks can be used to discriminate between children with SLI and their typically developing age-matched (TDAM) and younger (TDY) peers.


METHOD
Fourteen Cantonese-speaking children with SLI and 30 of their TDAM and TDY peers were compared on NWR and SR tasks. NWR of IN nonwords (CV combinations attested in the language) and OUT nonwords (CV combinations unattested in the language) were compared. SR performance was compared using 4 different scoring methods.


RESULTS
The SLI group did not score significantly lower than the TDAM group on the test of NWR (overall results were TDAM = SLI > TDY). There were nonsignificant group differences on IN syllables but not on OUT syllables. The results do not suggest a limitation in phonological working memory in Cantonese-speaking children with SLI. The SR task discriminated between children and their TDAM peers but not between children with SLI and their TDY peers matched for mean length of utterance.


CONCLUSIONS
SR but not NWR discriminates between children with SLI and their TDAM peers. Poorer NWR for English-speaking children with SLI might be attributable to weaker use of the redintegration strategy in word repetition. Further cross-linguistic investigations of processing strategies are required.

Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches <=SEP=> 

Data mining for yield enhancement in semiconductor manufacturing and an empirical study <=SEP=> During wafer fabrication, process data, equipment data, and lot history will be automatically or semi-automatically recorded and accumulated in database for monitoring the process, diagnosing faults, and managing manufacturing. However, in high-tech industry such as semiconductor manufacturing, many factors that are interrelated affect the yield of fabricated wafers. Engineers who rely on personal domain knowledge cannot find possible root causes of defects rapidly and effectively. This study aims to develop a framework for data mining and knowledge discovery from database that consists of a Kruskal–Wallis test, K-means clustering, and the variance reduction splitting criterion to investigate the huge amount of semiconductor manufacturing data and infer possible causes of faults and manufacturing process variations. The extracted information and knowledge is helpful to engineers as a basis for trouble shooting and defect diagnosis. We validated this approach with an empirical study in a semiconductor foundry company in Taiwan and the results demonstrated the practical viability of this approach. 2006 Elsevier Ltd. All rights reserved.

ING FOR TEXT CLASSIFICATION <=SEP=> Zero-shot Learners are models capable of predicting unseen classes. In this work, we propose a Zero-shot Learning approach for text categorization. Our method involves training model on a large corpus of sentences to learn the relationship between a sentence and embedding of sentence’s tags. Learning such relationship makes the model generalize to unseen sentences, tags, and even new datasets provided they can be put into same embedding space. The model learns to predict whether a given sentence is related to a tag or not; unlike other classifiers that learn to classify the sentence as one of the possible classes. We propose three different neural networks for the task and report their accuracy on the test set of the dataset used for training them as well as two other standard datasets for which no retraining was done. We show that our models generalize well across new unseen classes in both cases. Although the models do not achieve the accuracy level of the state of the art supervised models, yet it evidently is a step forward towards general intelligence in natural language processing.

Music, Intelligence and Artificiality <=SEP=> The discipline of Music-AI is defined as that activity which seeks to program computers to perform musical tasks in an intelligent, which possibly means humanlike way. A brief historical survey of different approaches within the discipline is presented. Two particular issues arise: the explicit representation of knowledge; and symbolic and subsymbolic representation and processing. When attempting to give a precise definition of Music-AI, it is argued that all musical processes must make some reference to human behaviour, and so Music-AI is a central rather than a peripheral discipline for musical computing. However, it turns out that the goals of Music-AI as first expressed, the mimicking of human behaviour, are impossible to achieve in full, and that it is impossible, in principle, for computers to pass a musical version of the Turing test. In practice, however, computers are used for their non-human-like behaviour just as much as their human-like behaviour, so the real goal of Music-AI must be reformulated. Furthermore, it is argued that the non-holistic analysis of human behaviour which this reformulation entails is actually informative for our understanding of human behaviour. Music-AI could also be fruitfully concerned with developing musical intelligences which were explicitly not human. Music-AI is then seen to be as much a creative enterprise as a scientific one.

Three-Dimensional Object Recognition from Single Two-Dimensional Images <=SEP=> A computer vision system has been implemented that can recognize threedimensional objects from unknown viewpoints in single gray-scale images. Unlike most other approaches, the recognition is accomplished without any attempt to reconstruct depth information bottom-up from the visual input. Instead, three other mechanisms are used that can bridge the gap between the two-dimensional image and knowledge of three-dimensional objects. First, a process of perceptual organization is used to form groupings and structures in the image that are likely to be invariant over a wide range of viewpoints. Second, a probabilistic ranking method is used to reduce the size of the search space during model based matching. Finally, a process of spatial correspondence brings the projections of three-dimensional models into direct correspondence with the image by solving for unknown viewpoint and model parameters. A high level of robustness in the presence of occlusion and missing data can be achieved through full application of a viewpoint consistency constraint. It is argued that similar mechanisms and constraints form the basis for recognition in human vision. This paper has been published in Artificial Intelligence, 31, 3 (March 1987), pp. 355–395.

Bispectrum estimation: A digital signal processing framework <=SEP=> It is the purpose of this tutorial paper to place bispectrum estimation in a digital signal processing framework in order to aid engineers in grasping the utility of the available bispectrum estimation techniques, to discuss application problems that can directly benefit from the use of the bispectrum, and to motivate research in this area. Three general reasons are behind the use of bispectrum in signal processing and are addressed in the paper: to extract information due to deviations from normality, to estimate the phase of parametric signals, and to detect and characterize the properties of nonlinear mechanisms that generate time series.

Database Security <=SEP=> Computer security is concerned with the ability of a computer system to enforce a security policy governing the disclosure, modification, or destruction of information. The security policy may be specific to the organization, or may be generic. For example, the DoD mandatory security (or multilevel security) poficies restrict access to classified information to cleared personnel. Discretionary security policies, on the other hand, define access restrictions based on the identity of users (or groups), the type of access (e.g., select, update, insert, delete), the specific object being accessed, and perhaps other factors (time of day, which application program is being used, etc.). Different types of users (system managers, database administrators, and ordinary users) may have different access rights to the data in the system. The access controls commonly found in most database systems are examples of discretionary access controls. This paper discusses discretionary security and mandatory security for database systems. We outline the current state of research in database security and briefly discuss some open research issues.

General duality between optimal control and estimation <=SEP=> Optimal control and estimation are dual in the LQG setting, as Kalman discovered, however this duality has proven difficult to extend beyond LQG. Here we obtain a more natural form of LQG duality by replacing the Kalman-Bucy filter with the information filter. We then generalize this result to non-linear stochastic systems, discrete stochastic systems, and deterministic systems. All forms of duality are established by relating exponentiated costs to probabilities. Unlike the LQG setting where control and estimation are in one-to-one correspondence, in the general case control turns out to be a larger problem class than estimation and only a sub-class of control problems have estimation duals. These are problems where the Bellman equation is intrinsically linear. Apart from their theoretical significance, our results make it possible to apply estimation algorithms to control problems and vice versa.

FAST: fast architecture sensitive tree search on modern CPUs and GPUs <=SEP=> In-memory tree structured index search is a fundamental database operation. Modern processors provide tremendous computing power by integrating multiple cores, each with wide vector units. There has been much work to exploit modern processor architectures for database primitives like scan, sort, join and aggregation. However, unlike other primitives, tree search presents significant challenges due to irregular and unpredictable data accesses in tree traversal.
 In this paper, we present FAST, an extremely fast architecture sensitive layout of the index tree. FAST is a binary tree logically organized to optimize for architecture features like page size, cache line size, and SIMD width of the underlying hardware. FAST eliminates impact of memory latency, and exploits thread-level and datalevel parallelism on both CPUs and GPUs to achieve 50 million (CPU) and 85 million (GPU) queries per second, 5X (CPU) and 1.7X (GPU) faster than the best previously reported performance on the same architectures. FAST supports efficient bulk updates by rebuilding index trees in less than 0.1 seconds for datasets as large as 64Mkeys and naturally integrates compression techniques, overcoming the memory bandwidth bottleneck and achieving a 6X performance improvement over uncompressed index search for large keys on CPUs.

Template Deformation for Point Cloud Fitting <=SEP=> The reconstruction of high-quality surface meshes from measured data is a vital stage in digital shape processing. We present a new approach to this problem that deforms a template surface to fit a given point cloud. Our method takes a template mesh and a point cloud as input, the latter typically shows missing parts and measurement noise. The deformation process is initially guided by user specified correspondences between template and data, then during iterative fitting new correspondences are established. This approach is based on a Laplacian setting for the template without need of any additional meshing of the data or cross-parameterization. The reconstructed surface fits to the point cloud while it inherits shape properties and topology of the template. We demonstrate the effectiveness of the approach for several point data sets from different sources.

Architectural Data Flow Analysis <=SEP=> Quality properties including performance, security and compliance are crucial for a system's success but are hard to prove, especially for complex systems. Data flow analyses support this but often only consider source code and thereby introduce high costs of repair. Data flow analyses on the architectural design level use call-and-return semantics or event-based communication between components but do not define data flows as first class entities or consider important runtime or deployment configurations. We propose introducing data flows as first class entities on the architectural level. Analyses ensure that systems meet the quality requirements even after changes in e.g. runtime or deployment configurations. Having data flows modeled as first class entities allows analyzing compliance with privacy laws, requirements for external service providers, and throughput requirements in big data scenarios on architectural level. The results allow early, cost-efficient fixing of issues.

In good company: how social capital makes organizations work [Book Review] <=SEP=> Don Cohen and Laurence Prusak define "social capital" in terms of connections among people. It emerges, they write, from "trust, mutual understanding, and shared values and behaviors that bind the members of human networks and communities and make more cooperative action possible." Continuing a theme that Prusak, executive director of the IBM Institute for Knowledge Management, has been developing, they offer examples of how social capital is created and destroyed and the role it plays in collective human endeavors, particularly in modern business. The authors suggest that this understanding is essential for developing models that respect and nourish human relationships. Seekers of a rigorous, quantified theoretical framework won't find it here. Instead, the book explores the role of trust, community, connectedness and loyalty in making companies work today.--Alan S. Kay

Amortized Inference and Learning in Latent Conditional Random Fields for Weakly-Supervised Semantic Image Segmentation <=SEP=> Conditional random fields (CRFs) are commonly employed as a post-processing tool for image segmentation tasks. The unary potentials of the CRF are often learnt independently by a classifier, thereby decoupling the inference in CRF from the training of classifier. Such a scheme works effectively, when pixel-level labelling is available for all the images. However, in absence of pixel-level labels, the classifier is faced with the uphill task of selectively assigning the image-level labels to the pixels of the image. Prior work often relied on localization cues, such as saliency maps, objectness priors, bounding boxes etc., to address this challenging problem. In contrast, we model the labels of the pixels as latent variables of a CRF. The pixels and the image-level labels are the observed variables of the latent CRF. We amortize the cost of inference in the latent CRF over the entire dataset, by training an inference network to approximate the posterior distribution of the latent variables given the observed variables. The inference network can be trained in an end-to-end fashion, and requires no localization cues for training. Moreover, unlike other approaches for weakly-supervised segmentation, the proposed model doesn’t require further post-processing. The proposed model achieves performance comparable with other approaches that employ saliency masks for the task of weakly-supervised semantic image segmentation on the challenging VOC 2012 dataset.

Common-centroid capacitor placement considering systematic and random mismatches in analog integrated circuits <=SEP=> One of the most important issues during the analog layout phase is to achieve accurate capacitance ratios. However, systematic and random mismatches will affect the accuracy of the capacitance ratios. A common-centroid placement is helpful to reduce the systematic mismatch, but it still needs the property of high dispersion to reduce the random mismatch [10]. To deal with this problem, we propose a simulated annealing [15] based approach to construct a common-centroid placement which exhibits the highest possible degree of dispersion. To facilitate this framework, we first propose the pair-sequence representation to represent a common-centroid placement. Then, we present three operations to perturb the representation, which can increase the degree of dispersion without breaking the common-centroid constraint in the resulting placement. Finally, to enhance the efficiency of our simulated annealing based approach, we propose three techniques to speed up our program. The experimental results show that our placements can simultaneously achieve smaller oxide-gradient-induced mismatch and larger overall correlation coefficients (i.e., higher degree of dispersion) than [10] in all test cases. Besides, our program can run much faster than [10] in larger benchmarks.

Exploring commonalities across participants in the neural representation of objects. <=SEP=> The question of whether the neural encodings of objects are similar across different people is one of the key questions in cognitive neuroscience. This article examines the commonalities in the internal representation of objects, as measured with fMRI, across individuals in two complementary ways. First, we examine the commonalities in the internal representation of objects across people at the level of interobject distances, derived from whole brain fMRI data, and second, at the level of spatially localized anatomical brain regions that contain sufficient information for identification of object categories, without making the assumption that their voxel patterns are spatially matched in a common space. We examine the commonalities in internal representation of objects on 3T fMRI data collected while participants viewed line drawings depicting various tools and dwellings. This exploratory study revealed the extent to which the representation of individual concepts, and their mutual similarity, is shared across participants.

agriGO: a GO analysis toolkit for the agricultural community <=SEP=> Gene Ontology (GO), the de facto standard in gene functionality description, is used widely in functional annotation and enrichment analysis. Here, we introduce agriGO, an integrated web-based GO analysis toolkit for the agricultural community, using the advantages of our previous GO enrichment tool (EasyGO), to meet analysis demands from new technologies and research objectives. EasyGO is valuable for its proficiency, and has proved useful in uncovering biological knowledge in massive data sets from high-throughput experiments. For agriGO, the system architecture and website interface were redesigned to improve performance and accessibility. The supported organisms and gene identifiers were substantially expanded (including 38 agricultural species composed of 274 data types). The requirement on user input is more flexible, in that user-defined reference and annotation are accepted. Moreover, a new analysis approach using Gene Set Enrichment Analysis strategy and customizable features is provided. Four tools, SEA (Singular enrichment analysis), PAGE (Parametric Analysis of Gene set Enrichment), BLAST4ID (Transfer IDs by BLAST) and SEACOMPARE (Cross comparison of SEA), are integrated as a toolkit to meet different demands. We also provide a cross-comparison service so that different data sets can be compared and explored in a visualized way. Lastly, agriGO functions as a GO data repository with search and download functions; agriGO is publicly accessible at http://bioinfo.cau.edu.cn/agriGO/.

Snakeboard motion planning with viscous friction and skidding <=SEP=> The snakeboard is a well-studied example for mechanical systems analysis, largely because of its simultaneous richness in behavior and simplicity in design. However, few snakeboard models incorporate dissipative friction in the traveling direction and skidding as a violation of the rigid nonholonomic constraints. In this paper we investigate these effects on trajectory planning by evaluating a previously proposed friction model as well as a novel skidding model based on the addition of Rayleigh dissipation functions. We show how these additions change the usual behavior of gaits in the forward planning problem, and incorporate the changes into the solutions of the inverse planning problem by utilizing body coordinates along with a curvature parameterization for trajectories.

Spatial but not verbal cognitive deficits at age 3 years in persistently antisocial individuals. <=SEP=> Previous studies have repeatedly shown verbal intelligence deficits in adolescent antisocial individuals, but it is not known whether these deficits are in place prior to kindergarten or, alternatively, whether they are acquired throughout childhood. This study assesses whether cognitive deficits occur as early as age 3 years and whether they are specific to persistently antisocial individuals. Verbal and spatial abilities were assessed at ages 3 and 11 years in 330 male and female children, while antisocial behavior was assessed at ages 8 and 17 years. Persistently antisocial individuals (N = 47) had spatial deficits in the absence of verbal deficits at age 3 years compared to comparisons (N = 133), and also spatial and verbal deficits at age 11 years. Age 3 spatial deficits were independent of social adversity, early hyperactivity, poor test motivation, poor test comprehension, and social discomfort during testing, and they were found in females as well as males. Findings suggest that early spatial deficits contribute to persistent antisocial behavior whereas verbal deficits are developmentally acquired. An early-starter model is proposed whereby early spatial impairments interfere with early bonding and attachment, reflect disrupted right hemisphere affect regulation and expression, and predispose to later persistent antisocial behavior.

Paddy diseases identification with texture analysis using fractal descriptors based on fourier spectrum <=SEP=> The efforts to increasing the quantity and quality of rice production are obstructed by the paddy disease. This research attempted to identify the four major paddy diseases in Indonesia (leaf blast, brown spot, bacterial leaf blight, and tungro) using fractal descriptors to analyze the texture of the lesions. The lesion images were extracted manually. The descriptors of `S' component of each lesion images then used in classification process using probabilistic neural networks. This techniques achieved at least 83.00% accuracy when identifying the diseases. This method has a potential to be used as one of the feature if it combined with other features, especially when two diseases with relatively same color involved.

Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries <=SEP=> The ACL Anthology Network (AAN)1 is a comprehensive manually curated networked database of citations and collaborations in the field of Computational Linguistics. Each citation edge in AAN is associated with one or more citing sentences. A citing sentence is one that appears in a scientific article and contains an explicit reference to another article. In this paper, we shed the light on the usefulness of AAN citing sentences for understanding research trends and summarizing previous discoveries and contributions. We also propose and motivate several different uses and applications of citing sentences.

Flexible UHF/VHF Vivaldi antenna for broadband and gas balloon applications <=SEP=> A Vivaldi flexible antenna with a −6 dB bandwidth from 150 MHz to 2000 MHz is introduced. The antenna is fabricated on a 60∗60 cm2 silicone substrate. In this paper, we present the design, realization and performances of this wide band and directive antenna. The proposed structure is lightweight, easy to realize and does not require any matching network. The targeted application is the radio-localization of signal source emissions from helium gas balloon. Six antennas are integrated on the bottom side of the balloon and the information is recovered with a cable that serves to its stabilization.

The Practical Use of BI Visualizations in Decision-making <=SEP=> Type of thesis: Master Degree Project in Accounting, 30 credits University: University of Gothenburg, School of Business, Economics and Law Semester: Spring 2017 Author: Kevin Larsson & Philip Kaber Supervisor: Elisabeth Frisk Title: The Practical Use of BI Visualizations in Decision-making Background and problem: Due to the constantly altering business environment, firms are trying to find ways of combining information technology (IT) and decision-making in order to grasp information of the complex and dynamic business environment. As a solution to this strenuousness; business intelligence (BI) systems serve to conduct more efficient and reliable decisions by the means of visualizations. One existing gap in the research of BI concerns the sociotechnical effects involved in the decision-making, in which knowledge of how BI visualizations are used and may be used in practice to support the decision-making have remained limited. Research aim: The research aim of the study is to investigate how BI visualizations are and may be used to support strategic and operational decisions. Further, the study addresses how BI visualizations are used in various decision contexts. Research questions: The following questions are addressed in order to achieve the research aim: (1) How are and may BI visualizations be used in strategic and operational decisions? (1a) Which visual elements are used depending on the task and decision category? (2) How are BI visualizations used in different decision contexts? Research design: A qualitative study was made through semi-structured interviews. In-depth interviews with six people from the purchasing company were conducted in order to understand how BI and visualizations are and may be used. Two people from the consultancy company were interviewed in order to understand how BI and visualizations may be used. Discussion and conclusion: The primary purpose of BI systems in the purchasing company is to support decision-making from a strategic perspective by providing the business with new insights and monitor business processes. However, the findings in this study also suggest that BI visualizations are used for operational errands. Strategic decisions are primary based on graphical representations, whereas operational decisions are based on data derived from tables and additional calculations in Excel. By creating more interactive, initiative, and humanizing visualizations, the end-users may discover new patterns of information and enhance the cognitive fit without being overwhelmed by data. BI visualizations may also be used in a larger extent to detect errors in data. The usage depends on the decision situation, in which decisions made in the fact-based contexts domains tend to be based on intuitions and BI visualizations, before responding to the business problem. This differs from the pattern-based management domains, in which actions are done prior to the analysis as there is lack of data. Further the findings indicate that several people are often involved in the decisions. The mental model of the respondents is not entirely fit in a cognitive aspect.