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The user can also automatically check for dwell times of plates (i.e., a parking meter) to deter overnight parking or assist with BOLOs (Be On The Lookout).\r\nThermal Anomaly Detection\r\n<ul> <li>Early Detection of Potential Fire Threat</li> <li>Unauthorized Individuals in Restricted Areas</li> <li>Suspicious Persons After Hours</li> <li>Running Vehicles</li> <li>Positive Identification Capture</li> </ul>\r\nAutomatic Signal Detection\r\nCombining this capability with Automatic License Plate Recognition as well as BOLO mug shot upload capabilities allows for unprecedented new capabilities in deterring unwanted behavior at your location.\r\nTwo-Way Intercom\r\nA Two-Way Intercom (human-to-human voice interaction) call can be initiated either by the KSOC user or a human presses the button on a machine. 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It contains selected recommendations and structures from Microsoft and other developers in the industry that ensure the successful implementation of data processing and analysis solutions. All this is to help companies take full advantage of their analytics software.","materialsDescription":"Data access\r\nData access is a generic term referring to a process which has both an IT-specific meaning and other connotations involving access rights in a broader legal and/or political sense. In the former it typically refers to software and activities related to storing, retrieving, or acting on data housed in a database or other repository. Two fundamental types of data access exist:\r\n<ol><li>sequential access (as in magnetic tape, for example)</li><li>random access (as in indexed media)</li></ol>\r\nData access crucially involves authorization to access different data repositories. Data access can help distinguish the abilities of administrators and users. For example, administrators may have the ability to remove, edit and add data, while general users may not even have "read" rights if they lack access to particular information.\r\nHistorically, each repository (including each different database, file system, etc.), might require the use of different methods and languages, and many of these repositories stored their content in different and incompatible formats.\r\nOver the years standardized languages, methods, and formats, have developed to serve as interfaces between the often proprietary, and always idiosyncratic, specific languages and methods. Such standards include SQL (1974- ), ODBC (ca 1990- ), JDBC, XQJ, ADO.NET, XML, XQuery, XPath (1999- ), and Web Services.\r\nSome of these standards enable translation of data from unstructured (such as HTML or free-text files) to structured (such as XML or SQL).\r\nStructures such as connection strings and DBURLs[1] can attempt to standardise methods of connecting to databases.\r\nA program for data analysis\r\nA data analysis program is a specially developed software that can analyze any information. This is usually a database program. It is within the database that you can store any information in any amount. A functional program allows incoming and stored data to analyze. Software development can be done for any tasks, which means that the program for data analysis can be customized for any activity. If, for example, you have a trade organization, then your data analysis program will analyze products, customers, funds, product balance, and more.\r\nOn-demand software delivery\r\nA type of software delivery service where the software is provided to the customer through a network (such as the Internet) as a service. On-demand software delivery is also called software on-demand. The terms on-demand software and Software as a Service (SaaS) are often used interchangeably.\r\nData access, analysis and delivery software are end-user oriented tools for ad hoc data access, analysis and reporting as well as production reporting.  These products are most commonly used by information consumers or power users rather than professional programmers.","iconURL":"https://old.roi4cio.com/fileadmin/user_upload/Data_Access__Analysis__and_Delivery_Software1.png"},{"id":229,"title":"Spatial Information Management Software","alias":"spatial-information-management-software","description":" A spatial database is a database that is optimized for storing and querying data that represents objects defined in a geometric space. Most spatial databases allow the representation of simple geometric objects such as points, lines and polygons. Some spatial databases handle more complex structures such as 3D objects, topological coverages, linear networks, and TINs. While typical databases have developed to manage various numeric and character types of data, such databases require additional functionality to process spatial data types efficiently, and developers have often added geometry or feature data types. The Open Geospatial Consortium (OGC) developed the Simple Features specification (first released in 1997) and sets standards for adding spatial functionality to database systems. The SQL/MM Spatial ISO/IEC standard is a part the SQL/MM multimedia standard and extends the Simple Features standard with data types that support circular interpolations.\r\nA geodatabase (also geographical database and geospatial database) is a database of geographic data, such as countries, administrative divisions, cities, and related information. Such databases can be useful for websites that wish to identify the locations of their visitors for customization purposes.\r\nDatabase systems use indexes to quickly look up values; however, this way of indexing data is not optimal for spatial queries. Instead, spatial databases use a spatial index to speed up database operations.\r\nIn addition to typical SQL queries such as SELECT statements, spatial databases can perform a wide variety of spatial operations. The following operations and many more are specified by the Open Geospatial Consortium standard:\r\n<ul><li>Spatial Measurements: Computes line length, polygon area, the distance between geometries, etc.</li><li>Spatial Functions: Modify existing features to create new ones, for example by providing a buffer around them, intersecting features, etc.</li><li>Spatial Predicates: Allows true/false queries about spatial relationships between geometries. Examples include "do two polygons overlap" or 'is there a residence located within a mile of the area we are planning to build the landfill?'</li><li>Geometry Constructors: Creates new geometries, usually by specifying the vertices (points or nodes) which define the shape.</li><li>Observer Functions: Queries which return specific information about a feature such as the location of the center of a circle.</li></ul>\r\nSome databases support only simplified or modified sets of these operations, especially in cases of NoSQL systems like MongoDB and CouchDB.","materialsDescription":"What is the spatial index?\r\nSpatial indices are used by spatial databases (databases which store information related to objects in space) to optimize spatial queries. Conventional index types do not efficiently handle spatial queries such as how far two points differ, or whether points fall within a spatial area of interest. Common spatial index methods include:\r\n<ul><li>Geohash</li><li>HHCode</li><li>Grid (spatial index)</li><li>Z-order (curve)</li><li>Quadtree</li><li>Octree</li><li>UB-tree</li><li>R-tree: Typically the preferred method for indexing spatial data.[citation needed] Objects (shapes, lines and points) are grouped using the minimum bounding rectangle (MBR). Objects are added to an MBR within the index that will lead to the smallest increase in its size.</li><li>R+ tree</li><li>R* tree</li><li>Hilbert R-tree</li><li>X-tree</li><li>kd-tree</li><li>m-tree – an m-tree index can be used for the efficient resolution of similarity queries on complex objects as compared using an arbitrary metric.</li><li>Binary space partitioning (BSP-Tree): Subdividing space by hyperplanes.</li></ul>","iconURL":"https://old.roi4cio.com/fileadmin/user_upload/Spatial_Information_Management_Software.png"},{"id":227,"title":"Advanced Analytics Software","alias":"advanced-analytics-software","description":" Advanced analytics is a part of data science that uses high-level methods and tools to focus on projecting future trends, events, and behaviors. This gives organizations the ability to perform advanced statistical models such as ‘what-if’ calculations, as well as future-proof various aspects of their operations.\r\nThe term is an umbrella for several sub-fields of analytics that work together in their predictive capabilities.\r\nThe major areas that make up advanced analytics are predictive data analytics, big data, and data mining. The process of advanced analytics includes all three areas at various times.\r\nData mining is a key aspect of advanced analytics, providing the raw data that will be used by both big data and predictive analytics. Big data analytics are useful in finding existing insights and creating connections between data points and sets, as well as cleaning data.\r\nFinally, predictive analytics can use these clean sets and existing insights to extrapolate and make predictions and projections about future activity, trends, and consumer behaviors.\r\nAdvanced analytics also include newer technologies such as machine learning and artificial intelligence, semantic analysis, visualizations, and even neural networks. Taken together, they help advanced analytics software create an accurate enough canvas to make reliable predictions and generate actionable BI insights on a deeper level.","materialsDescription":"<h1 class=\"align-center\">A list of tips on how to manage the process of building an advanced analytics program</h1>\r\n<ul><li>Start with a proof-of-concept project to demonstrate the potential business value of analytics applications.</li><li>Take training seriously. New data management and analytics skills likely will be needed, especially if big data platforms and tools like SAS advanced analytics tools are involved.</li><li>Develop processes to ensure that business units are ready to act on analytical findings so the work of data scientists and other analysts doesn't go to waste.</li><li>Monitor and assess advanced and predictive analytics software on a regular basis to make sure the data being analyzed is still relevant and the analytical models being run against it are still valid.</li></ul>\r\n<h1 class=\"align-center\">Advanced analytics tools</h1>\r\nThere are a variety of advanced analytics tools to choose from that offer different advantages based on the use case. They generally break down into two categories: open source and proprietary.\r\nOpen source tools have become a go-to option for many data scientists doing machine learning and prescriptive analytics. They include programming languages, as well as computing environments, including Hadoop and Spark. Users typically say they like open source advanced analytics tools because they are generally inexpensive to operate, offer strong functionality and are backed by a user community that continually innovates the tools.\r\nOn the proprietary side, vendors including Microsoft, IBM and the SAS Institute all offer advanced analytics tools. Most require a deep technical background and understanding of mathematical techniques.\r\nIn recent years, however, a crop of self-service analytics tools has matured to make functionality more accessible to business users. Tableau, in particular, has become a common tool. While its functionality is more limited than deeper technical tools, it does enable users to conduct cluster analyses and other advanced analyses.","iconURL":"https://old.roi4cio.com/fileadmin/user_upload/Advanced_Analytics_Software.png"},{"id":225,"title":"End-User Query, Reporting, and Analysis","alias":"end-user-query-reporting-and-analysis","description":" Class systems of Query & Reporting are designed to form queries to information systems in user terms, as well as their execution, integration of data from different sources, viewing data with the possibilities of detailing and summarizing and building full-fledged reports, both on-screen and printed. Visualization of query results can be presented to the user in various forms - flat or multidimensional tables, graphs, charts, various specialized interfaces.\r\nEnd-user tools for querying and building reports are delivered in two ways:\r\n<ul><li>as part of OLAP-systems</li><li>in the form of specialized Query & Reporting</li></ul>\r\nVirtually every OLAP class system is equipped with Query & Reporting. These funds can be either embedded in the main product or separated into a separate product.\r\nThere are also specialized systems for generating and distributing reports. At the same time, these systems incorporate their own OLAP-means. Therefore, to draw a clear line between OLAP-systems and systems of class Query & Reporting is almost impossible.","materialsDescription":" What are the reporting tools?\r\nReporting tools are widely used to support decision making and to measure organizational and team performance. Companies use them for financial consolidation, for evaluation of strategies and policies and often just for plain reporting. Today most of these tools are integrated with Business Intelligence tools.\r\nWhat are the advantages of these tools for your organization?\r\nReporting tools allow companies to create attractive reports easily. In tabular or graphical format. With data from Excel, a data warehouse or the organization’s ERP system. With the reports containing the right information, people are able to manage and improve the business processes more easily.\r\nWhich reporting tools are available in the market?\r\nThere are many Business Intelligence Reporting tools in the market available. This list of reporting tools shows you a complete overview of all the major tools currently available and thoroughly studied by our industry analysts. To name a few: Cognos Query and reporting, Oracle Enterprise Reporting, Crystal Reports, Microsoft Reporting Services (SSRS), etc.\r\nIs there a difference between reporting tools and BI tools?\r\nAlmost every company, large or small, needs a reporting tool or they have already one in place. But, what is the difference between reporting software and Business Intelligence tools? They are not the same. In general reporting tools tell us where we have been but are not very good at analyzing data (at real time) and telling us where we are going (predictive).\r\nWhat is the difference between a query and a report?\r\nA query is a way to create a list of accounts or journal entries that match your criteria. Requests serve as an advanced search engine that finds and groups accounts or journal entries and can only create a list.\r\nA report is a detailed report of a group of accounts or journal entries. 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This gives organizations the ability to perform advanced statistical models such as ‘what-if’ calculations, as well as future-proof various aspects of their operations.\r\nThe term is an umbrella for several sub-fields of analytics that work together in their predictive capabilities.\r\nThe major areas that make up advanced analytics are predictive data analytics, big data, and data mining. The process of advanced analytics includes all three areas at various times.\r\nData mining is a key aspect of advanced analytics, providing the raw data that will be used by both big data and predictive analytics. Big data analytics are useful in finding existing insights and creating connections between data points and sets, as well as cleaning data.\r\nFinally, predictive analytics can use these clean sets and existing insights to extrapolate and make predictions and projections about future activity, trends, and consumer behaviors.\r\nAdvanced analytics also include newer technologies such as machine learning and artificial intelligence, semantic analysis, visualizations, and even neural networks. Taken together, they help advanced analytics software create an accurate enough canvas to make reliable predictions and generate actionable BI insights on a deeper level.","materialsDescription":"<h1 class=\"align-center\">A list of tips on how to manage the process of building an advanced analytics program</h1>\r\n<ul><li>Start with a proof-of-concept project to demonstrate the potential business value of analytics applications.</li><li>Take training seriously. 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