{"component": "clause", "props": {"groups": [{"snippet_links": [{"key": "the-truth", "type": "definition", "offset": [182, 191]}, {"key": "data-sources", "type": "definition", "offset": [268, 280]}, {"key": "data-sets", "type": "definition", "offset": [473, 482]}, {"key": "regression-testing", "type": "definition", "offset": [636, 654]}, {"key": "resolving-issues", "type": "clause", "offset": [678, 694]}, {"key": "data-integrity", "type": "definition", "offset": [737, 751]}], "snippet": "Under an \u201cAnalytics Services\u201d engagement, we will assist you with designing and configuring a scalable and reusable federated enterprise data layer that supports a single version of the truth; tasks may include configuring and optimizing connections to databases, big data sources, NoSQL sources, and enterprise applications to access enterprise data for use in analytics and mobility applications; designing an optimized in-memory strategy and publishing high performance data sets to Analysts, Data Scientists and Developers, and Architects, so they can build analytics, models and applications faster on trusted data; and conducting regression testing, reviewing the schema, resolving issues, and implementing a process for on- going data integrity management.", "samples": [{"hash": "mjKXs1PKGA", "uri": "/contracts/mjKXs1PKGA#analytics-services", "label": "Software License and Services Agreement", "score": 32.8418521517, "published": true}, {"hash": "2JedQmT44pu", "uri": "/contracts/2JedQmT44pu#analytics-services", "label": "Services Agreement", "score": 32.6803474645, "published": true}, {"hash": "8Tmzw8eDY8j", "uri": "/contracts/8Tmzw8eDY8j#analytics-services", "label": "Software License and Services Agreement", "score": 30.5506754871, "published": true}], "size": 21, "hash": "d18df639aeb2332b660950b3cdda884c", "id": 1}, {"snippet_links": [{"key": "visual-flow-creator", "type": "clause", "offset": [321, 340]}, {"key": "your-applications", "type": "definition", "offset": [382, 399]}, {"key": "the-training", "type": "clause", "offset": [503, 515]}, {"key": "conditions-of-assets", "type": "clause", "offset": [601, 621]}, {"key": "based-on", "type": "clause", "offset": [647, 655]}, {"key": "applications-for", "type": "clause", "offset": [820, 836]}, {"key": "condition-monitoring", "type": "clause", "offset": [849, 869]}, {"key": "early-warning", "type": "clause", "offset": [871, 884]}, {"key": "statistical-analysis", "type": "clause", "offset": [992, 1012]}, {"key": "period-of-time", "type": "clause", "offset": [1061, 1075]}, {"key": "to-provide", "type": "definition", "offset": [1114, 1124]}, {"key": "key-performance-indicators", "type": "definition", "offset": [1150, 1176]}, {"key": "sensor-data", "type": "definition", "offset": [1186, 1197]}, {"key": "sequence-of-events", "type": "clause", "offset": [1209, 1227]}, {"key": "related-to", "type": "clause", "offset": [1290, 1300]}, {"key": "gas-turbines", "type": "definition", "offset": [1359, 1371]}, {"key": "applicable-to", "type": "clause", "offset": [1400, 1413]}, {"key": "industrial-applications", "type": "definition", "offset": [1420, 1443]}, {"key": "the-function", "type": "clause", "offset": [1560, 1572]}, {"key": "information-sources", "type": "definition", "offset": [1589, 1608]}, {"key": "the-service", "type": "clause", "offset": [1651, 1662]}, {"key": "applied-for", "type": "definition", "offset": [1670, 1681]}, {"key": "historical-data", "type": "clause", "offset": [1682, 1697]}, {"key": "automated-processing", "type": "clause", "offset": [1713, 1733]}, {"key": "new-data", "type": "definition", "offset": [1746, 1754]}, {"key": "if-required", "type": "definition", "offset": [1945, 1956]}, {"key": "the-original", "type": "definition", "offset": [2027, 2039]}, {"key": "common-issues", "type": "clause", "offset": [2105, 2118]}, {"key": "in-time", "type": "definition", "offset": [2119, 2126]}, {"key": "data-quality", "type": "clause", "offset": [2189, 2201]}, {"key": "the-area", "type": "clause", "offset": [2271, 2279]}, {"key": "data-analytics", "type": "clause", "offset": [2377, 2391]}], "snippet": "Analytics Services are available via their respective MindSphere APIs and provide basic and advanced analytical functions for time series data such as Anomaly Detection, Event Analytics, KPI Calculation, Signal Calculation, Signal Validation and Trend Prediction. They can be utilized in an interactive mode e.g. via the Visual Flow Creator (workflow tool for calling APIs) or from your Applications. \uf0b7 Anomaly Detection aims to support the detection of unexpected behavior of processes and assets. For the training of anomaly detection, normal data is sufficient. Normal data represents the standard conditions of assets. Furthermore, clustering based on anomaly detection techniques allow human interaction and integration of domain knowledge (e.g. by labeling of new clusters and/or anomalies). A developer can build Applications for process and condition monitoring, early warning functionality and detection of fault conditions without explicit definitions. \uf0b7 Event Analytics provides a statistical analysis for visualizing the most frequent events over a period of time. \uf0b7 KPI Calculation offers an easy way to provide various calculations for key performance indicators based on sensor data as well as sequence of events (i.e. from control/automation systems). The characteristic is related to an ISO 3977-9:1999 standard which is in fact dedicated to gas turbines. The characteristic is also applicable to other industrial applications. It is possible to provide automated annotation for time series data for many common characteristics. Additionally, the function can combine two information sources, numerical sensor data as well as events. The Service can be applied for historical data as well as the automated processing of incoming new data. \uf0b7 Signal Calculation offers commonly used missing value handling strategies, for instance, removal and interpolation. It calculates a descriptive summary of a sequence of signal values and if required, it derives new signal values by shifting, smoothing and transforming the original ones. \uf0b7 Signal Validation provides functions that help to detect common issues in time series data. Signal Validation can be used for optimizing the data quality. \uf0b7 Trend Prediction is a forecasting framework that may be useful in the area of process and condition monitoring. 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For the training of anomaly detection, normal data is sufficient. Normal data represents the standard conditions of assets. Furthermore, clustering based on anomaly detection techniques allow human interaction and integration of domain knowledge (e.g. by labeling of new clusters and/or anomalies). A developer can build Applications for process and condition monitoring, early warning functionality and detection of fault conditions without explicit definitions. \u2022 Event Analytics provides a statistical analysis for visualizing the most frequent events over a period of time. \u2022 KPI Calculation offers an easy way to provide various calculations for Key Performance Indicators based on sensor data as well as sequence of events (i.e. from control/automation systems). The characteristic of these KPI calculations is related to an ISO 3977-9:1999 standard which is in fact dedicated to gas turbines. The characteristic is also applicable to other industrial applications. It is possible to provide automated annotation for time series data for many common characteristics. Additionally, the function can combine two information sources, numerical sensor data as well as events. The Service can be applied for historical data as well as the automated processing of incoming new data. \u2022 Signal Calculation offers commonly used missing value handling strategies, for instance, removal and interpolation. It calculates a descriptive summary of a sequence of signal values and if required, it derives new signal values by shifting, smoothing and transforming the original ones. \u2022 Signal Validation provides functions that help to detect common issues in time series data. Signal Validation can be used for optimizing the data quality. \u2022 Trend Prediction is a forecasting framework that may be useful in the area of process and condition monitoring. Also, seasonality and trend removal is an essential task of data analytics pre-processing. Analytics Services can be utilized in either an interactive mode or batch mode, e.g. via the Visual Flow Creator (workflow tool for calling APIs) or from your Applications: \u2022 Batch mode: Allows processing of up to 1 000 000 data points with one single API call including all dimensions with a response time between 40 seconds and several hours (depending on algorithm complexity). \u2022 Interactive mode: Allows processing of up to 20 000 data points with one single API call with a response time below 10 seconds. 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These services may collect, store and use de- identified information in order to help us understand things like how often you use the Service and Artsonia Website, the events that occur within the application, usage, performance data, and from where the application was downloaded. Legal Requirements: We may use or disclose information, including Personal Information, if we have a good faith belief that doing so is necessary to comply with the law, such as complying with a subpoena or other legal process. We may need to disclose Personal Information where, in good faith, we think it is necessary to protect the rights, property, or safety of Artsonia, our employees, our community, or others, or to prevent violations of our Terms or other agreements. This includes, without limitation, exchanging information with other companies and organizations for fraud protection or responding to government requests. Where appropriate, we may notify users about the legal requests, unless (i) providing notice is prohibited by the legal process itself, by court order we receive, or by applicable law; (ii) we believe that providing notice would be futile, ineffective, create a risk of injury or bodily harm to an individual or group, or create or increase a risk of fraud upon Artsonia, or its users. In instances where we comply with legal requests without notice for these reasons, we will attempt to notify that user about the request after the fact where appropriate and where we determine in good faith that we are no longer prevented from doing so. Sharing with Artsonia Companies: Over time, Artsonia may grow and reorganize. We may share your information, including Personal Information with affiliates such as a parent company, subsidiaries, joint venture partners or other companies that we control or that are under common control with us, in which case we will require those companies to agree to use your Personal Information in a way that is consistent with this Privacy Policy. With your consent: Other than the cases above, we won't disclose your Personal Information for any purpose unless you consent to it. Additionally, as discussed above, we will never sell or rent your Personal Information to advertisers or other third parties.", "samples": [{"hash": "jJdmboutckL", "uri": "/contracts/jJdmboutckL#analytics-services", "label": "Data Privacy Agreement", "score": 32.5735901289, "published": true}, {"hash": "7KUuOwAX2qO", "uri": "/contracts/7KUuOwAX2qO#analytics-services", "label": "Data Privacy Agreement", "score": 32.3376237282, "published": true}, {"hash": "knL5EMbaUu5", "uri": "/contracts/knL5EMbaUu5#analytics-services", "label": "Data Privacy Agreement", "score": 26.787816564, "published": true}], "size": 3, "hash": "1f9ef756c4d5626ac3a16614eccbf0a8", "id": 4}, {"snippet_links": [{"key": "with-respect-to", "type": "clause", "offset": [0, 15]}, {"key": "each-purchase", "type": "clause", "offset": [16, 29]}, {"key": "receipt-of", "type": "clause", "offset": [33, 43]}, {"key": "company-agrees", "type": "clause", "offset": [63, 77]}, {"key": "services-and", "type": "clause", "offset": [120, 132]}, {"key": "other-interactions", "type": "clause", "offset": [147, 165]}, {"key": "information-provided", "type": "clause", "offset": [175, 195]}, {"key": "resulting-from-the", "type": "clause", "offset": [209, 227]}, {"key": "comply-with", "type": "clause", "offset": [239, 250]}, {"key": "state-and-local-laws", "type": "clause", "offset": [275, 295]}, {"key": "rules-and-regulations", "type": "definition", "offset": [307, 328]}, {"key": "including-without-limitation", "type": "clause", "offset": [330, 358]}, {"key": "communications-act-of-1934", "type": "clause", "offset": [364, 390]}, {"key": "as-amended", "type": "definition", "offset": [392, 402]}, {"key": "telephone-consumer-protection-act", "type": "clause", "offset": [408, 441]}, {"key": "implementing-regulations", "type": "definition", "offset": [455, 479]}, {"key": "issued-by", "type": "definition", "offset": [480, 489]}, {"key": "federal-communications-commission", "type": "clause", "offset": [490, 523]}, {"key": "telemarketing-and-consumer-fraud-and-abuse-prevention-act", "type": "definition", "offset": [529, 586]}, {"key": "federal-trade-commission", "type": "definition", "offset": [592, 616]}, {"key": "telemarketing-sales-rule", "type": "definition", "offset": [619, 643]}, {"key": "federal-and-state-laws-and-regulations", "type": "clause", "offset": [731, 769]}, {"key": "goods-or-services", "type": "clause", "offset": [822, 839]}, {"key": "consumer-protection-laws", "type": "definition", "offset": [859, 883]}, {"key": "without-limiting-the-generality-of-subsection", "type": "clause", "offset": [1001, 1046]}, {"key": "company-will", "type": "clause", "offset": [1058, 1070]}, {"key": "for-purposes-of", "type": "clause", "offset": [1247, 1262]}, {"key": "a-person", "type": "clause", "offset": [1275, 1283]}, {"key": "eligibility-for-insurance", "type": "clause", "offset": [1286, 1311]}, {"key": "fair-credit-reporting-act", "type": "definition", "offset": [1377, 1402]}, {"key": "required-licenses", "type": "clause", "offset": [1438, 1455]}, {"key": "required-by", "type": "definition", "offset": [1491, 1502]}, {"key": "authority-to", "type": "clause", "offset": [1552, 1564]}, {"key": "company-business", "type": "clause", "offset": [1573, 1589]}, {"key": "the-products-and-services", "type": "clause", "offset": [1645, 1670]}, {"key": "subject-of-the", "type": "clause", "offset": [1684, 1698]}, {"key": "consent-agreements", "type": "clause", "offset": [1786, 1804]}, {"key": "government-investigations", "type": "definition", "offset": [1841, 1866]}, {"key": "and-company", "type": "clause", "offset": [1868, 1879]}, {"key": "in-any-action", "type": "clause", "offset": [1953, 1966]}, {"key": "other-proceeding", "type": "clause", "offset": [1996, 2012]}], "snippet": "With respect to each purchase or receipt of Leads or Services, Company agrees and warrant that the use of the Leads and Services and any calls and other interactions with and information provided to consumers resulting from the Leads will comply with all applicable federal, state and local laws, statutes, rules and regulations, including without limitation, the Communications Act of 1934, as amended, the Telephone Consumer Protection Act (\u201cTCPA\u201d) and implementing regulations issued by Federal Communications Commission, the Telemarketing and Consumer Fraud and Abuse Prevention Act, the Federal Trade Commission\u2019s Telemarketing Sales Rule, the Controlling the Assault of Non-Solicited Pornography and Marketing Act, and other federal and state laws and regulations governing the marketing, promotion, and/or sales of goods or services, including general consumer protection laws and regulations, or other consumer protection laws that prohibit unfair, deceptive, or misleading acts or practices; without limiting the generality of subsection (a) above, Company will not make any calls to any individual listed on any federal or state national Do-Not- Call (DNC) registry unless an exemption applies; Company will not use any lead information for purposes of determining a person\u2019s eligibility for insurance, credit, employment or otherwise in any manner that violates the Fair Credit Reporting Act; Company have obtained any and all required licenses, permits, and other authorizations required by any law, regulation, or government or regulatory authority to conduct Company business as presently conducted, including offering and selling the products and services that are the subject of the lead; Company have disclosed the existence of any federal or state decrees, orders, or consent agreements, and any pending formal or informal government investigations, and Company further represents and warrants that if Company become involved or named in any action, investigation, complaint or other proceeding by or before any", "samples": [{"hash": "6YbWgsKiyCf", "uri": "/contracts/6YbWgsKiyCf#analytics-services", "label": "Consolidated Terms and Conditions of Services", "score": 24.1033538672, "published": true}], "size": 3, "hash": "7653210c4f37c30d3c300aba08086dbe", "id": 5}, {"snippet_links": [{"key": "service-includes", "type": "clause", "offset": [42, 58]}, {"key": "access-to", "type": "definition", "offset": [59, 68]}, {"key": "third-party", "type": "clause", "offset": [69, 80]}, {"key": "strategic-services", "type": "definition", "offset": [141, 159]}, {"key": "as-specified", "type": "clause", "offset": [193, 205]}, {"key": "order-form", "type": "definition", "offset": [224, 234]}], "snippet": "If Customer\u2019s subscription to the Moovweb Service includes access to third party analytics platforms (\u201cAnalytics Services\u201d) or other premium strategic services that utilize Analytics Services (as specified in the applicable Order Form or SOW), the following terms shall apply:", "samples": [{"hash": "hBgMJ4VvpLD", "uri": "/contracts/hBgMJ4VvpLD#analytics-services", "label": "Platform Subscription Terms of Service", "score": 33.6685371609, "published": true}, {"hash": "1xhQ4YMhmO1", "uri": "/contracts/1xhQ4YMhmO1#analytics-services", "label": "Platform Subscription Agreement", "score": 32.69677167, "published": true}], "size": 2, "hash": "155c653c8e939c78136823011527bb89", "id": 6}, {"snippet_links": [{"key": "the-truth", "type": "definition", "offset": [182, 191]}, {"key": "data-sources", "type": "definition", "offset": [268, 280]}, {"key": "data-sets", "type": "definition", "offset": [474, 483]}, {"key": "regression-testing", "type": "definition", "offset": [637, 655]}, {"key": "resolving-issues", "type": "clause", "offset": [679, 695]}, {"key": "data-integrity", "type": "definition", "offset": [737, 751]}], "snippet": "Under an \u201cAnalytics Services\u201d engagement, we will assist you with designing and configuring a scalable and reusable federated enterprise data layer that supports a single version of the truth; 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