gatterbauer.name valuation and analysis
Robots.txt Information
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Meta Tags
| Title | Wolfgang Gatterbauer, Northeastern |
| Description | Wolfgang Gatterbauer academic web |
| Keywords | Wolfgang Gatterbauer, Northeastern University, Khoury College of Computer Sciences, Databases |
Server Information
| WebSite | |
| Host IP | 217.160.0.185 |
| Location | Germany |
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gatterbauer.name Valuation
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US$1,163,411
Last updated: 2023-05-16 12:19:18
gatterbauer.name has Semrush global rank of 9,097,651. gatterbauer.name has an estimated worth of US$ 1,163,411, based on its estimated Ads revenue. gatterbauer.name receives approximately 134,240 unique visitors each day. Its web server is located in Germany, with IP address 217.160.0.185. According to SiteAdvisor, gatterbauer.name is safe to visit. |
Traffic & Worth Estimates
| Purchase/Sale Value | US$1,163,411 |
| Daily Ads Revenue | US$1,074 |
| Monthly Ads Revenue | US$32,218 |
| Yearly Ads Revenue | US$386,611 |
| Daily Unique Visitors | 8,950 |
| Note: All traffic and earnings values are estimates. | |
DNS Records
| Host | Type | TTL | Data |
| gatterbauer.name. | A | 3599 | IP: 217.160.0.185 |
| gatterbauer.name. | NS | 21600 | NS Record: ns1033.ui-dns.biz. |
| gatterbauer.name. | NS | 21600 | NS Record: ns1033.ui-dns.org. |
| gatterbauer.name. | NS | 21600 | NS Record: ns1033.ui-dns.com. |
| gatterbauer.name. | NS | 21600 | NS Record: ns1033.ui-dns.de. |
| gatterbauer.name. | MX | 3600 | MX Record: 10 mx01.kundenserver.de. |
| gatterbauer.name. | MX | 3600 | MX Record: 10 mx00.kundenserver.de. |
HtmlToTextCheckTime:2023-05-16 12:19:18
| Toggle navigation Home Papers Projects Students Other DATA lab Teaching & Advising --> Wolfgang Gatterbauer Associate Professor Khoury College of Computer Sciences 440 Huntington Avenue Northeastern University Boston, MA 02115 --> wgatterbauer@northeastern.edu +1 (617) 373-2462 Office: 450 West Village H DATA lab @ Northeastern --> I am working on the theory of scalable data management . One of my goals is to extend the capabilities of modern data management systems in generic ways to allow them to support novel functionalities that seem hard at first. Examples of such functionalities are managing provenance, trust, explanations, and uncertain or inconsistent data. To support these functionalities, I am interested in understanding the the fundamental algebraic properties that allow algorithms to scale with the size of data by leveraging structure in data: Given a large data or knowledge base, what types of questions can be answered efficiently? And what do we do about those |
HTTP Headers
HTTP/1.1 301 Moved Permanently Content-Type: text/html; charset=iso-8859-1 Connection: keep-alive Keep-Alive: timeout=15 Date: Wed, 26 Jan 2022 09:57:40 GMT Server: Apache Location: https://gatterbauer.name/ HTTP/2 200 content-type: text/html content-length: 8099 date: Wed, 26 Jan 2022 09:57:40 GMT server: Apache last-modified: Sun, 23 Jan 2022 16:57:26 GMT etag: "1fa3-5d642bf205442" accept-ranges: bytes |
gatterbauer.name Whois Information
**** Registry Domain ID: 134467349_DOMAIN_NAME-VRSN Domain Name: GATTERBAUER.NAME Registrar: IONOS SE Registrar IANA ID: 83 Domain Status: clientTransferProhibited https://icann.org/epp#clientTransferProhibited >>> Last update of whois database: 2022-01-25T23:16:26Z <<< |